-----------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\C
> h6-Violence.log
  log type:  text
 opened on:  26 Jul 2023, 19:55:49

. 
.  
.         ******************************
.         **** Set directory, seed *****
.         ******************************
.                 set more off 

.                 set matsize 1000
set matsize ignored.
    Matrix sizes are no longer limited by c(matsize) in modern Statas.  Matrix
    sizes are now limited by edition of Stata.  See limits for more details.

.                 global seed ="984353"

.                 set scheme plotplain

.                 cd "$dir"
C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction

.                 
.                 
.         ********************************
.         *** Election violence trends ***
.         ********************************
.                 use pers-use,clear

.                 tsset cowcode year 

Panel variable: cowcode (unbalanced)
 Time variable: year, 1991 to 2020, but with gaps
         Delta: 1 unit

.                 gen opolar = l1polar if year==minyr
(1,823 missing values generated)

.                 egen ipolar = max(opolar),by(lid)
(86 missing values generated)

.                 *drop if GNB==1
.                 drop b  

.                 gen majoritarian = v2elparlel==0 if v2elparlel~=.

.                 gen proportional = v2elparlel==1 if v2elparlel~=.

.                 sort cowcode year 

.                 gen nextyearleader = current_leader[_n+1] if cowcode ==cowcode[_n
> +1] 
(106 missing values generated)

.                 gen election = v2xel_elecparl==1 | v2xel_elecpres==1

.                 gen exelection = v2xel_elecpres==1 | (v2xel_elecparl==1 & pres==0
> )

.                 egen std_v2x_clphy = std(v2x_clphy)

.                 egen std_v2elintim = std(v2elintim)
(1,647 missing values generated)

. 
.                 
.                 * Exclude presidential elections in Parliamentary systems | Parl 
> election in Pres system *
.                 replace exelection = 0 if (country=="Austria" & (year==2004 | yea
> r==2016)) | (country=="Slovakia" & (year==2009 | year==2019)) | ///
>                         (country=="Macedonia" & year==1999) | (country=="Croatia"
>  & year==2009) | (country=="Bulgaria" & (year==1992 | year==1996)) | ///
>                         (country=="Romania" & (year==2008 | year==2016)) | (count
> ry=="Finland" & year==2018) |  ///
>                         (country=="Iceland" & (year==2004 | year==2012)) | (count
> ry=="Lebanon" & year==2009)
(14 real changes made)

.                 
.                 tsset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1991 to 2020, but with gaps
         Delta: 1 unit

.                 gen fpid =f.electing_p_id
(180 missing values generated)

.                 gen f2pid =f2.electing_p_id
(294 missing values generated)

.                 gen flid = f.lid
(132 missing values generated)

.                 gen f2lid = f2.lid
(251 missing values generated)

.                 gen xlose  = (fpid~=electing_p_id)  & (lid~=flid) if fpid~=.  & f
> lid~=. & exelection==1
(1,831 missing values generated)

.                 recode xlose (0=1) if (f2pid~=electing_p_id)  & (lid~=f2lid) & f2
> pid~=.  & f2lid & exelection==1
(38 changes made to xlose)

.                 gen time  =year-1990

.                 gen lose = v2eltvrexo==2 if exelection~=. & v2eltvrexo~=.
(1,683 missing values generated)

.                 replace lose = 0 if lose==1 & lid==flid & lid==f2lid
(28 real changes made)

.                 replace lose = 0 if xlose==0 & v2eltvrexo==2 & pres==1           
>          /* leader re-elected but different party */
(6 real changes made)

.                 replace lose = 1 if xlose==1 & (v2eltvrexo==1 | v2eltvrexo==0) & 
> pres==1  /* leader loses and different party wins executive */
(10 real changes made)

.                 
.                  * Double checked these codings *
.                 replace lose = 1 if (country=="Dominican Republic" & year ==2000)
>  |  (country=="Dominican Republic" & year ==2004) |  ///
>                         (country=="Guatemala" & year ==1999) |  (country=="Costa 
> Rica" & year ==1994 ) |  (country=="Costa Rica" & year ==1998 ) | ///
>                         (country=="Panama" & (year==1994 | year==1999)) | (countr
> y=="Ecuador" & (year==1992|year==1996|year==1998 )) | ///
>                         (country=="Chile" & year ==1999) | (country=="Uruguay" & 
> year ==2019 ) | (country=="Belgium" & year==1999) | ///
>                         (country=="Netherlands" & (year==1994 | year==2010)) | (c
> ountry=="Austria" & year==1999) | (country=="Guinea Bissau" & year==2009) | ///
>                         (country=="France" & year==2017) | (country=="Portugal" &
>  year==2006) | ///
>                         (country=="Macedonia" & (year==1998 | year==2002 | year==
> 2004)) | (country=="Croatia" & year==2000) |   ///
>                         (country=="Federal Republic of Yugoslavia" & (year==2003 
> | year==2008)) | (country=="Kosovo" & (year==2017 | year==2019)) | ///
>                         (country=="Slovenia" & (year==1992 | year==2018)) | (coun
> try=="Moldova" & year==2009) | (country=="Romania" & year==1992) | ///
>                         (country=="Latvia" & (year==1995 | year==1998)) | (countr
> y=="Ukraine" & year==2004) | (country=="Iceland" & (year==1991 | year==2017)) | /
> //
>                         (country=="Benin" & (year==2006 | year==2016)) | (country
> =="Nigeria" & year==2015) | (country=="Malawi" & year==2014) | ///
>                         (country=="Madagascar" & (year==1996 | year==2001 | year=
> =2018)) | (country=="Iraq" & year==2018) | (country=="Tunisia" & year==2014) | //
> /     
>                         (country=="Mongolia" & year==1997) | (country=="South Kor
> ea" & year==1992) | (country=="Sri Lanka" & year==2019) | ///
>                         (country=="Pakistan" & (year==1993 | year==1997 | year==2
> 013))          
(35 real changes made)

.                 replace lose = 0 if (country=="Haiti" & year==1995) | (country=="
> Colombia" & year==2002) | (country=="Netherlands" & year==2006) | ///
>                    (country=="Belgium" & year==2007) | (country=="Japan" & year==
> 1993) | (country=="France" & year==2002) | ///
>                    (country=="Portugal" & year==2011) | (country=="Slovakia" & (y
> ear==1994 | year==2016)) | ///
>                    (country=="Macedonia" & (year==2004 | year==2016 | year==2019)
> ) | (country=="Croatia" & year==2016) | ///
>                    (country=="Federal Republic of Yugoslavia" & (year==2002 | yea
> r==2007)) | (country=="Slovenia" & (year==2002)) | ///
>                    (country=="Moldova" & year==2014) | (country=="Estonia" & year
> ==2015) | (country=="Latvia" & (year==2010 | year==2014)) | ///
>                    (country=="Georgia" & year==2018) | (country=="Finland" & year
> ==1994) | (country=="Sweden" & year==2010) | ///
>                    (country=="Iceland" & (year==1995 | year==2003)) | (country=="
> Benin" & year==2011) | (country=="Lesotho" & year==1998) | ///
>                    (country=="Israel" & year==2006)
(6 real changes made)

.                   
.                 * Drop France 2017: incumbent ruling party was the Socialists but
>  the violence occurred during 
.                 * the 2nd round that pitted Le Pen (National Front) against Macro
> n (En Marche) -- and Le Pen lost
.                 drop if country=="France" & year==2017
(1 observation deleted)

.                    
.                 * Election violence outcome *
.                 gen elviolence = v2elpeace*-1
(1,647 missing values generated)

.                 gen eviolence =  v2elpeace_ord<4 if v2elpeace_ord~=.
(1,647 missing values generated)

.                 gen elviolence_ord = v2elpeace_ord*-1 + 5
(1,647 missing values generated)

.                 tab elviolence_ord

elviolence_ |
        ord |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |        512       68.82       68.82
          2 |        137       18.41       87.23
          3 |         65        8.74       95.97
          4 |         27        3.63       99.60
          5 |          3        0.40      100.00
------------+-----------------------------------
      Total |        744      100.00

.                 list country year elviolence_ord persparty lose if  (elviolence_o
> rd==4 | elviolence_ord==5) & exelection==1,clean noobs

        country   year   elviol~d   perspa~y   lose  
          Haiti   2015          4   .6963265      1  
      Guatemala   1996          4   .5506856      .  
       Honduras   2017          5   .3042722      0  
       Colombia   1994          4   .1619093      0  
       Colombia   1998          4   .2116281      1  
       Colombia   2002          4    .587021      0  
       Colombia   2006          4   .6963265      0  
       Colombia   2010          4   .6963265      1  
        Albania   1997          4   .7023838      1  
    Ivory Coast   2020          4   .5354734      0  
         Guinea   2020          5   .8906565      0  
        Nigeria   2003          4   .6963265      0  
        Nigeria   2011          4   .4672859      0  
          Kenya   2007          5   .4580351      0  
         Zambia   2016          4   .4580351      0  
           Iraq   2018          4   .2116281      1  
    Afghanistan   2019          4          1      0  
       Pakistan   2013          4   .4580351      1  
     Bangladesh   2014          4   .3042722      0  
      Sri Lanka   1999          4   .4580351      0  
          Nepal   1994          4   .6337572      1  
          Nepal   1999          4   .3042722      0  
          Nepal   2008          4   .3042722      1  
       Thailand   2014          4   .8125014      .  

.                 
.                 * Rescale state-led violence for same mean/sd as non-state violen
> ce *
.                 sum frepress  if exelection==1 & lose~=.

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    frepress |        514   -.8643234    1.498483  -4.939681   2.541588

.                 local m1 = r(mean)

.                 local sd1=r(sd)

.                 sum eviolence  if exelection==1  & lose~=.

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   eviolence |        588    .2993197      .45835          0          1

.                 local m2 = r(mean)

.                 local sd2=r(sd)

.                 replace frepress = (frepress+abs(`m1')+`m2' + ((abs(`m1')+`m2')/(
> `sd1'/.9))) /(`sd1'/`sd2')
(2,119 real changes made)

.                 sum frepress eviolence  if exelection==1  & lose~=.

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    frepress |        514    .3053292      .45835  -.9412247   1.347116
   eviolence |        588    .2993197      .45835          0          1

.                 
.                 label def region 1 "Eastern Europe and Central Asia" 2 "Latin Ame
> rica and the Caribbean"  ///
>                         3 "Middle East and North Africa" 4 "Sub-Saharan Africa" 5
>  "Western Europe and North America" ///
>                         6 "Asia and Pacific" ,replace

.                 label val pregion region

.                 table pregion, stat(mean eviolence)

----------------------------------------------
                                   |      Mean
-----------------------------------+----------
pregion                            |          
  Eastern Europe and Central Asia  |  .1504854
  Latin America and the Caribbean  |  .4539474
  Middle East and North Africa     |  .3333333
  Sub-Saharan Africa               |  .7916667
  Western Europe and North America |   .026455
  Asia and Pacific                 |  .5584416
  Total                            |   .311828
----------------------------------------------

.                 keep if exelection==1  & lose~=.
(1,803 observations deleted)

.                 
.  
.                 * Summary stats *
.                 tab  eviolence lose,row col

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

           |         lose
 eviolence |         0          1 |     Total
-----------+----------------------+----------
         0 |       211        201 |       412 
           |     51.21      48.79 |    100.00 
           |     69.64      70.53 |     70.07 
-----------+----------------------+----------
         1 |        92         84 |       176 
           |     52.27      47.73 |    100.00 
           |     30.36      29.47 |     29.93 
-----------+----------------------+----------
     Total |       303        285 |       588 
           |     51.53      48.47 |    100.00 
           |    100.00     100.00 |    100.00 

.                 table pregion if exelection==1 & lose~=.,stat(n lose) stat(mean l
> ose) stat(mean eviolence)   stat(mean  frepress)

----------------------------------------------------------------------------------------------------
                                   |  Number of nonmissing values                 Mean              
                                   |                         lose       lose   eviolence    frepress
-----------------------------------+----------------------------------------------------------------
pregion                            |                                                                
  Eastern Europe and Central Asia  |                          171   .4853801    .1461988    .2540342
  Latin America and the Caribbean  |                          114    .622807    .4385965    .5690837
  Middle East and North Africa     |                           15   .5333333    .2666667    .9685893
  Sub-Saharan Africa               |                           74   .3378378    .7972973    .5428323
  Western Europe and North America |                          157   .4076433    .0254777   -.1207663
  Asia and Pacific                 |                           57   .5964912    .5964912    .6815186
  Total                            |                          588   .4846939    .2993197    .3053292
----------------------------------------------------------------------------------------------------

.                 
.                 * Election violence time trends plot *
.                 global bw=4

.                 global v= "eviolence"

.                 sum  eviolence if lose~=.

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   eviolence |        588    .2993197      .45835          0          1

.                 twoway   lpoly $v year,bw($bw)lpat(solid)lcol(gs1)ylab(.2(.1).4) 
> tit(All elections) ///
>                         ytit(Incidence of election violence)xtit(Year) saving(h1.
> gph,replace)  
(file h1.gph not found)
file h1.gph saved

.                         
.                 twoway (lpoly eviolence year if exelection==1,ytit("Election viol
> ence") ///
>                         ylab(.15(.15).45)bw($bw)xtit(Year)) ///
>                         (lpoly frepress year if exelection==1,lpat(solid)bw($bw) 
> ///
>                         legend(lab(1 "Non-state violence")lab(2 "State-led repres
> sion")pos(6)ring(0)) ///
>                         tit(State and non-state election violence))

. 
.                 twoway  (lpoly $v year if lose==0,bw($bw)lpat(solid)lcol(gs10)) /
> //
>                         (lpoly $v year if lose==1,saving(h2.gph,replace)   ///
>                         bw($bw)lpat(solid)lcol(gs10)lcol(gs1)ylab(.2(.1).4) tit(B
> y incumbent victory/loss) ///
>                         ytit(Incidence of election violence)xtit(Year)   ///
>                         legend(lab(1 "Ruling party {bf:wins}")lab(2 "Ruling party
>  {bf:loses}")pos(11)ring(0)size(small)))       
(file h2.gph not found)
file h2.gph saved

.                 gr combine h1.gph h2.gph  , note("589 Executive elections, 1991-2
> 020", ///
>                         size(vsmall)pos(6))col(2) xsize(6)iscale(.9)tit(Non-state
>  election violence)

.                 gr export "$dir\golden\Ch6-Election-Violence-Trends.pdf",as(pdf)r
> eplace                 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h6-Election-Violence-Trends.pdf saved as PDF format

.         
.         *******************************************
.         *** Non-state election-related violence ***
.         *******************************************
.                 global cvar  = "v2elintim ivdem ld pres major l1v2x_clphy l1v2cav
> iol time"

. 
.                 * No interaction *
.                 logit eviolence lose persparty if exelection==1,cluster(lid)    

Iteration 0:  Log pseudolikelihood = -358.84864  
Iteration 1:  Log pseudolikelihood = -342.80093  
Iteration 2:  Log pseudolikelihood = -342.60592  
Iteration 3:  Log pseudolikelihood = -342.60582  
Iteration 4:  Log pseudolikelihood = -342.60582  

Logistic regression                                     Number of obs =    588
                                                        Wald chi2(2)  =  22.89
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -342.60582                       Pseudo R2     = 0.0453

                                  (Std. err. adjusted for 431 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   eviolence | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        lose |  -.1122831   .1808506    -0.62   0.535    -.4667438    .2421776
   persparty |   2.451205   .5125668     4.78   0.000     1.446592    3.455817
       _cons |  -2.102695    .306435    -6.86   0.000    -2.703297   -1.502094
------------------------------------------------------------------------------

.                 est store viol0

.                 logit eviolence lose persparty v2elintim if exelection==1,cluster
> (lid)  

Iteration 0:  Log pseudolikelihood = -358.84864  
Iteration 1:  Log pseudolikelihood = -214.73155  
Iteration 2:  Log pseudolikelihood = -206.31473  
Iteration 3:  Log pseudolikelihood =  -206.2355  
Iteration 4:  Log pseudolikelihood = -206.23549  

Logistic regression                                     Number of obs =    588
                                                        Wald chi2(3)  = 167.34
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -206.23549                       Pseudo R2     = 0.4253

                                  (Std. err. adjusted for 431 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   eviolence | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        lose |   .5063008   .2462836     2.06   0.040     .0235938    .9890077
   persparty |   .6462663   .6860787     0.94   0.346    -.6984232    1.990956
   v2elintim |  -2.056807   .1676848   -12.27   0.000    -2.385463   -1.728151
       _cons |   .2692153   .4588732     0.59   0.557    -.6301597     1.16859
------------------------------------------------------------------------------

.                 est store viol1

.                 logit eviolence lose persparty $cvar if exelection==1,cluster(lid
> )      

Iteration 0:  Log pseudolikelihood = -355.61752  
Iteration 1:  Log pseudolikelihood =  -170.7443  
Iteration 2:  Log pseudolikelihood = -155.79965  
Iteration 3:  Log pseudolikelihood = -155.29776  
Iteration 4:  Log pseudolikelihood = -155.29685  
Iteration 5:  Log pseudolikelihood = -155.29685  

Logistic regression                                     Number of obs =    579
                                                        Wald chi2(10) = 153.32
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -155.29685                       Pseudo R2     = 0.5633

                                  (Std. err. adjusted for 426 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   eviolence | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        lose |   .3337231   .2848708     1.17   0.241    -.2246133    .8920596
   persparty |   .7317139   .8346729     0.88   0.381    -.9042149    2.367643
   v2elintim |  -1.656685   .2949392    -5.62   0.000    -2.234755   -1.078614
       ivdem |  -1.120648   1.959931    -0.57   0.567    -4.962043    2.720747
          ld |   .6809946   .2340784     2.91   0.004     .2222093     1.13978
        pres |   .9794471   .3067554     3.19   0.001     .3782175    1.580677
majoritarian |    .378945   .3773361     1.00   0.315    -.3606203     1.11851
 l1v2x_clphy |   4.424255   1.571813     2.81   0.005     1.343559    7.504951
  l1v2caviol |   .7861704   .1693131     4.64   0.000     .4543229    1.118018
        time |  -.0210964   .0186246    -1.13   0.257    -.0576001    .0154072
       _cons |  -1.739191   1.434442    -1.21   0.225    -4.550647    1.072264
------------------------------------------------------------------------------

.                 est store viol2

.                 * Interaction *
.                 gen loseXpers = lose*persparty

.                 logit eviolence persparty lose loseXpers $cvar if exelection==1,c
> luster(lid)

Iteration 0:  Log pseudolikelihood = -355.61752  
Iteration 1:  Log pseudolikelihood = -169.76815  
Iteration 2:  Log pseudolikelihood = -153.67628  
Iteration 3:  Log pseudolikelihood = -153.01396  
Iteration 4:  Log pseudolikelihood = -153.01334  
Iteration 5:  Log pseudolikelihood = -153.01334  

Logistic regression                                     Number of obs =    579
                                                        Wald chi2(11) = 143.94
                                                        Prob > chi2   = 0.0000
Log pseudolikelihood = -153.01334                       Pseudo R2     = 0.5697

                                  (Std. err. adjusted for 426 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
   eviolence | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.9116634   1.040074    -0.88   0.381    -2.950172    1.126845
        lose |  -1.397545   .8763418    -1.59   0.111    -3.115144     .320053
   loseXpers |   3.100867   1.512147     2.05   0.040     .1371122    6.064621
   v2elintim |  -1.727367   .3096327    -5.58   0.000    -2.334236   -1.120498
       ivdem |  -1.280107   1.920231    -0.67   0.505    -5.043691    2.483476
          ld |   .7398486   .2359468     3.14   0.002     .2774014    1.202296
        pres |   1.054045   .3092558     3.41   0.001     .4479146    1.660175
majoritarian |   .3492427   .3782062     0.92   0.356    -.3920277    1.090513
 l1v2x_clphy |   4.527575   1.540242     2.94   0.003     1.508756    7.546395
  l1v2caviol |   .8302574   .1764085     4.71   0.000      .484503    1.176012
        time |  -.0238905   .0182529    -1.31   0.191    -.0596655    .0118846
       _cons |   -.829777   1.439705    -0.58   0.564    -3.651546    1.991992
------------------------------------------------------------------------------

.                 est store viol3

.                 lincom lose + loseXpers*.3

 ( 1)  [eviolence]lose + .3*[eviolence]loseXpers = 0

------------------------------------------------------------------------------
   eviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.4672853   .4705198    -0.99   0.321    -1.389487    .4549166
------------------------------------------------------------------------------

.                 lincom lose + loseXpers*.7

 ( 1)  [eviolence]lose + .7*[eviolence]loseXpers = 0

------------------------------------------------------------------------------
   eviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .7730613   .3644896     2.12   0.034     .0586748    1.487448
------------------------------------------------------------------------------

.                 
.                 label var lose `""Incumbent" "loses    " "election  ""'

.                 label var persparty `""Ruling party " "{bf:personalism}""'

.                 label var v2elintim  `""Government" "electoral   " "intimidation 
> ""'

.                 label var loseXpers `""Incumbent loses" "X           " "Personali
> sm  ""'

. 
.                 coefplot (viol0, msymbol(d))(viol1, msymbol(P)) (viol2, msymbol(T
> )) (viol3, msymbol(Oh)), order(persparty)  ///
>                                 drop(_cons  ivdem ld pres majoritarian l1v2x_clph
> y l1v2caviol time) xline(0) msymbol(d) mfcolor(white) grid(glcolor(gs15)) ///
>                                 levels(95 90) legend(lab(3 "Baseline")lab(6 "+ Go
> vt intimidation") ///
>                                 lab(9 "+ covariates")lab(12 "Interaction")  order
> (3 6 9 12) ///
>                                 size(small) pos(6) col(2) ring(1)) xsize(2) ysize
> (2) xlab(-3(3)6)  ///
>                                 xtitle("        Coefficient estimate", size(small
> ))  ///
>                                 ciopts(lwidth(thin)) aspectratio(1.1) scale(.75) 
> title(Election violence, size(medium) height(2))
(note:  named style P not found in class symbol, default attributes used)

.                 gr export "$dir\golden\T-Election-Violence-Estimates.pdf",as(pdf)
> replace                
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Election-Violence-Estimates.pdf saved as PDF format

. 
.                 * Split sample *                
.                 logit eviolence persparty $cvar if exelection==1 & lose==0 

Iteration 0:  Log likelihood = -184.18682  
Iteration 1:  Log likelihood =  -84.33961  
Iteration 2:  Log likelihood = -75.204991  
Iteration 3:  Log likelihood = -74.674039  
Iteration 4:  Log likelihood = -74.672786  
Iteration 5:  Log likelihood = -74.672786  

Logistic regression                                     Number of obs =    298
                                                        LR chi2(9)    = 219.03
                                                        Prob > chi2   = 0.0000
Log likelihood = -74.672786                             Pseudo R2     = 0.5946

------------------------------------------------------------------------------
   eviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -1.390899   1.226069    -1.13   0.257    -3.793949    1.012151
   v2elintim |  -1.761781   .4507013    -3.91   0.000     -2.64514   -.8784231
       ivdem |  -.6252622   2.490118    -0.25   0.802    -5.505803    4.255279
          ld |   .4643167   .3469414     1.34   0.181     -.215676    1.144309
        pres |   .7844161   .4739194     1.66   0.098    -.1444489    1.713281
majoritarian |   .0614008    .527183     0.12   0.907    -.9718589     1.09466
 l1v2x_clphy |   4.112338   2.212687     1.86   0.063    -.2244476    8.449125
  l1v2caviol |   1.080795   .2449732     4.41   0.000     .6006565    1.560934
        time |   .0069541   .0309106     0.22   0.822    -.0536296    .0675379
       _cons |  -.2946826   1.869527    -0.16   0.875    -3.958888    3.369523
------------------------------------------------------------------------------

.                 logit eviolence persparty $cvar if exelection==1 & lose==1 

Iteration 0:  Log likelihood = -171.39792  
Iteration 1:  Log likelihood = -82.513605  
Iteration 2:  Log likelihood =  -74.57741  
Iteration 3:  Log likelihood = -74.186774  
Iteration 4:  Log likelihood =  -74.18637  
Iteration 5:  Log likelihood =  -74.18637  

Logistic regression                                     Number of obs =    281
                                                        LR chi2(9)    = 194.42
                                                        Prob > chi2   = 0.0000
Log likelihood = -74.18637                              Pseudo R2     = 0.5672

------------------------------------------------------------------------------
   eviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |   2.644716   1.108275     2.39   0.017     .4725375    4.816895
   v2elintim |  -1.667296   .4222564    -3.95   0.000    -2.494904   -.8396891
       ivdem |  -2.360524   2.613924    -0.90   0.366     -7.48372    2.762673
          ld |   1.011867   .3479781     2.91   0.004     .3298427    1.693892
        pres |    1.23113   .4670315     2.64   0.008     .3157653    2.146495
majoritarian |   .8070319   .5502458     1.47   0.142      -.27143    1.885494
 l1v2x_clphy |    5.48092   2.140543     2.56   0.010     1.285533    9.676307
  l1v2caviol |   .5995898    .215255     2.79   0.005     .1776978    1.021482
        time |  -.0490307   .0273158    -1.79   0.073    -.1025688    .0045073
       _cons |  -2.730165   1.857841    -1.47   0.142    -6.371467    .9111361
------------------------------------------------------------------------------

.                 
.                 * Kernel *
.                 krls eviolence lose persparty $cvar if exelection==1,d(k)lambda(.
> 4065 )

Pointwise Derivatives                                      Number of obs =      579
>  
                                                           Lambda        =    .4065
>  
                                                           Tolerance     =        0
>  
                                                           Sigma         =       10
>  
                                                           Eff. df       =    128.4
>  
                                                           R2            =    .7895
>  
                                                           Looloss       =    103.9

    eviolence |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
--------------+--------------------------------------------------------------------
        *lose |  .000543   .025389    0.021    0.983   -.049249   .000271   .058257
>   
    persparty | -.003072    .05018   -0.061    0.951   -.148407   .026722   .170932
>   
    v2elintim | -.100287   .019844   -5.054    0.000   -.180129  -.094343  -.020931
>   
        ivdem | -.287012   .124605   -2.303    0.022   -.678283  -.299303   .105614
>   
           ld |   .07124   .013976    5.097    0.000    .024361   .055977   .106655
>   
        *pres |  .071416   .029217    2.444    0.015   -.011059   .051818   .150437
>   
*majoritarian |  .084359   .038326    2.201    0.028   -.046384   .041212    .21649
>   
  l1v2x_clphy |  .204593    .13861    1.476    0.140   -.240469   .175813    .63774
>   
   l1v2caviol |  .053835   .009815    5.485    0.000   -.031047   .035418   .121133
>   
         time |  -.00303   .001167   -2.597    0.010   -.009517   -.00217   .002933
>   
--------------+--------------------------------------------------------------------


.                 twoway lpolyci k_lose persparty,yline(0)legend(off)bw(.15)ytit(Ma
> rginal effect of losing an election)xtit(Ruling party personalism)

.                 drop k_*

. 
.                 * Plot interaction *
.                 gen x=.
(588 missing values generated)

.                 gen b=.
(588 missing values generated)

.                 gen hi5=.
(588 missing values generated)

.                 gen lo5=.
(588 missing values generated)

.                 gen n=_n

.                 xi:interflex eviolence lose persparty $cvar if exelection==1,clus
> ter(lid)nbin(2)cutoffs(.545)
p value of Wald test: 0.4221

.                         mat list r(estBin)

r(estBin)[2,5]
            x0    bin_marg      bin_se    bin_CI_l    bin_CI_u
r1   .33365002  -.00538261   .02888303  -.06199231    .0512271
r2   .70238376    .0851299   .04009147   .00655206   .16370773

.                 xi:interflex eviolence lose persparty $cvar if exelection==1,type
> (kernel)bw(.22) 

.                         mat list r(margeff)

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1           0   .03194704   .04151449  -.04941987   .11331395
 r2   .02040816   .02674589    .0396159  -.05089984   .10439163
 r3   .04081633   .02194418   .03784727  -.05223512   .09612347
 r4   .06122449   .01756501   .03621682  -.05341865   .08854866
 r5   .08163265   .01362787   .03472996   -.0544416   .08169735
 r6   .10204082   .01014838   .03338928  -.05529341   .07559016
 r7   .12244898   .00713797   .03219443  -.05596195    .0702379
 r8   .14285714   .00460377   .03114229    -.056434   .06564153
 r9   .16326531   .00254839   .03022716  -.05669576   .06179254
r10   .18367347   .00096999   .02944111  -.05673352    .0586735
r11   .20408163  -.00013772   .02877437  -.05653445   .05625901
r12    .2244898  -.00078524   .02821587  -.05608733   .05451685
r13   .24489796  -.00098695   .02775372  -.05538325   .05340935
r14   .26530612  -.00076069   .02737578  -.05441623   .05289485
r15   .28571429  -.00012722   .02707009  -.05318361   .05292917
r16   .30612245   .00089037   .02682534  -.05168634   .05346707
r17   .32653061   .00226725   .02663126  -.04992907   .05446356
r18   .34693878   .00397753   .02647891  -.04792017   .05587523
r19   .36734694   .00599488   .02636093  -.04567159   .05766135
r20    .3877551   .00829313   .02627182   -.0431987   .05978496
r21   .40816327   .01084696   .02620813  -.04052002   .06221395
r22   .42857143   .01363243   .02616859  -.03765706   .06492193
r23   .44897959   .01662769   .02615435   -.0346339   .06788928
r24   .46938776   .01981355   .02616908   -.0314769     .071104
r25   .48979592   .02317418     .026219  -.02821412   .07456247
r26   .51020408   .02669765   .02631293  -.02487474   .07827005
r27   .53061224   .03037653   .02646209  -.02148821   .08224126
r28   .55102041   .03420824   .02667978  -.01808316   .08649964
r29   .57142857   .03819538   .02698088  -.01468617   .09107692
r30   .59183673   .04234567    .0273811   -.0113203   .09601164
r31    .6122449   .04667178   .02789611  -.00800359   .10134715
r32   .63265306   .05119084   .02854055  -.00474761   .10712929
r33   .65306122    .0559237   .02932712   -.0015564    .1134038
r34   .67346939   .06089406   .03026582   .00157415   .12021397
r35   .69387755   .06612749   .03136346   .00465625   .12759874
r36   .71428571   .07165053   .03262356   .00770952   .13559153
r37   .73469388   .07748978   .03404657   .01075974   .14421982
r38   .75510204   .08367137   .03563032   .01383722   .15350551
r39    .7755102   .09022052   .03737077   .01697515   .16346588
r40   .79591837    .0971615    .0392627   .02020803   .17411498
r41   .81632653   .10451788   .04130044    .0235705   .18546525
r42   .83673469   .11231296   .04347853   .02709661   .19752931
r43   .85714286   .12057055   .04579219   .03081951   .21032159
r44   .87755102   .12931585   .04823771   .03477167   .22386003
r45   .89795918    .1385765    .0508127   .03898545   .23816755
r46   .91836735   .14838372   .05351617   .04349396   .25327347
r47   .93877551    .1587735   .05634863   .04833221   .26921479
r48   .95918367   .16978783   .05931204   .05353837   .28603729
r49   .97959184    .1814758    .0624097   .05915503   .30379658
r50           1   .19389482    .0656462   .06523064   .32255901

.                         mat r =r(margeff)

.                         forval i=1/50 {
  2.                                 qui replace x  = r[`i',1] if n==`i'
  3.                                 qui replace b  = r[`i',2] if n==`i'
  4.                                 qui replace hi5  = r[`i',5] if n==`i'
  5.                                 qui replace lo5  = r[`i',4] if n==`i'
  6.                         }

.                  twoway (hist persparty if e(sample)==1,ytit(" ",axis(2))yaxis(2)
> yscale(range(0 50)axis(2))ylab(0 " ",axis(2))) ///
>                         (rarea hi5 lo5 x if x<=50,sort col(gs14) yaxis(1)yscale(a
> lt)yscale(alt axis(2)) ) ///
>                         (line b x if x<=50,sort lcol(gs1)lpat(solid)yline(0)ylab(
> -.1(.1).3) ///
>                         xtit(Ruling party personalism)legend(off)ytit("Marginal e
> ffect of incumbent {bf:losing an election}",size(small)) ///
>                         tit("Incumbent losing an election increases election viol
> ence" "when the ruling party is personalist" ) ///
>                         note("580 Executive elections, 1991-2020",      size(smal
> l)pos(6))) 

.                  gr export "$dir\golden\Ch6-Election-Related-Non-State-Violence.p
> df",as(pdf)replace             
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h6-Election-Related-Non-State-Violence.pdf saved as PDF format

.  
.                 * Show ruling party personalism does not boost election loss gove
> rnment intimidation *
.                 xi:interflex std_v2elintim lose persparty ivdem ld pres l1v2x_clp
> hy l1v2caviol time if exelection==1, 
p value of Wald test: 0.2193

.                         mat r =r(estBin)

.                         forval i=1/3 {
  2.                                 qui replace x  = r[`i',1] if n==`i'
  3.                                 qui replace b  = r[`i',2] if n==`i'
  4.                                 qui replace hi5  = r[`i',5] if n==`i'
  5.                                 qui replace lo5  = r[`i',4] if n==`i'
  6.                         }

.                  twoway  (rspike hi5 lo5 x if n<=3,sort col(gs1)   ) ///
>                         (scatter b x if n<=3,sort yline(0)ylab(-.1(.1).4)xlab(0(.
> 2)1) ///
>                         xtit(Ruling party personalism)legend(off)ytit("Marginal e
> ffect of incumbent {bf:losing an election}",size(small)) ///
>                         tit("Government intimidation" ) ///
>                         note("579 Executive elections, 1991-2020",      size(smal
> l)pos(6))saving(h1.gph,replace))               
file h1.gph saved

.                         
.                 xi:interflex std_v2x_clphy lose persparty ivdem ld pres l1v2x_clp
> hy l1v2caviol time if exelection==1,
p value of Wald test: 0.1047

.                         mat r =r(estBin)

.                         forval i=1/3 {
  2.                                 qui replace x  = r[`i',1] if n==`i'
  3.                                 qui replace b  = r[`i',2] if n==`i'
  4.                                 qui replace hi5  = r[`i',5] if n==`i'
  5.                                 qui replace lo5  = r[`i',4] if n==`i'
  6.                         }

.                  twoway  (rspike hi5 lo5 x if n<=3,sort col(gs1)   ) ///
>                         (scatter b x if n<=3,sort yline(0)ylab(-.1(.1).4)xlab(0(.
> 2)1) ///
>                         xtit(Ruling party personalism)legend(off)ytit("Marginal e
> ffect of incumbent {bf:losing an election}",size(small)) ///
>                         tit("Government repression" ) ///
>                         note("579 Executive elections, 1991-2020",      size(smal
> l)pos(6))saving(h2.gph,replace))       
file h2.gph saved

.                 gr combine h1.gph h2.gph,xsize(8)tit("Incumbent losing associated
>  with government intimidation" "but not government repression")

.                 gr export "$dir\golden\T-Election-Repression-Intimidation.pdf",as
> (pdf)replace   
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Election-Repression-Intimidation.pdf saved as PDF format

.                         
.                 * Exclude Africa *
.                 xi:interflex eviolence lose persparty $cvar if exelection==1 & (c
> owcode<400 | cowcode>=600),type(kernel)bw(.22)  

.                         mat r =r(margeff)

.                         forval i=1/50 {
  2.                                 qui replace x  = r[`i',1] if n==`i'
  3.                                 qui replace b  = r[`i',2] if n==`i'
  4.                                 qui replace hi5  = r[`i',5] if n==`i'
  5.                                 qui replace lo5  = r[`i',4] if n==`i'
  6.                         }

.                  twoway (hist persparty if e(sample)==1,ytit(" ",axis(2))yaxis(2)
> yscale(range(0 50)axis(2))ylab(0 " ",axis(2))) ///
>                         (rarea hi5 lo5 x if x<=50,sort col(gs14) yaxis(1)yscale(a
> lt)yscale(alt axis(2)) ) ///
>                         (line b x if x<=50,sort lcol(gs1)lpat(solid)yline(0)ylab(
> -.1(.1).5) ///
>                         xtit(Ruling party personalism)legend(off)ytit("Marginal e
> ffect of incumbent {bf:losing an election}",size(small)) ///
>                         tit("Exclude Africa") ///
>                         note("505 Executive elections, 1991-2020",      size(smal
> l)pos(6))saving(h1.gph,replace)) 
file h1.gph saved

.                 * Exclude Europe *
.                 xi:interflex eviolence lose persparty $cvar if exelection==1 & (c
> owcode<200 | cowcode>=400),type(kernel)bw(.22)  

.                         mat r =r(margeff)

.                         forval i=1/50 {
  2.                                 qui replace x  = r[`i',1] if n==`i'
  3.                                 qui replace b  = r[`i',2] if n==`i'
  4.                                 qui replace hi5  = r[`i',5] if n==`i'
  5.                                 qui replace lo5  = r[`i',4] if n==`i'
  6.                         }

.                  twoway (hist persparty if e(sample)==1,ytit(" ",axis(2))yaxis(2)
> yscale(range(0 50)axis(2))ylab(0 " ",axis(2))) ///
>                         (rarea hi5 lo5 x if x<=50,sort col(gs14) yaxis(1)yscale(a
> lt)yscale(alt axis(2)) ) ///
>                         (line b x if x<=50,sort lcol(gs1)lpat(solid)yline(0)ylab(
> -.1(.1).5) ///
>                         xtit(Ruling party personalism)legend(off)ytit("Marginal e
> ffect of incumbent {bf:losing an election}",size(small)) ///
>                         tit("Exclude Europe" ) ///
>                         note("306 Executive elections, 1991-2020",      size(smal
> l)pos(6))saving(h2.gph,replace)) 
file h2.gph saved

.                         
.                 gr combine h1.gph h2.gph,xsize(8)tit("Incumbent losing an electio
> n increases election violence" "when the ruling party is personalist")

.                 gr export "$dir\golden\T-Election-Violence-Global.pdf",as(pdf)rep
> lace           
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -Election-Violence-Global.pdf saved as PDF format

. 
.         ***********************************************
.         *** Losing party accepting an election loss ***
.         ***********************************************
.          twoway lpolyci v2elaccept persparty if exelection==1 & lose==1

.          gen notaccept = v2elaccept_ord<3 if v2elaccept_ord~=0 & v2elaccept_ord~=
> .
(1 missing value generated)

.          ttest notaccept if exelection==1,by(create)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |     435    .0528736    .0107418    .2240387    .0317611     .073986
       1 |     152        .125    .0269135    .3318122    .0718243    .1781757
---------+--------------------------------------------------------------------
Combined |     587    .0715503    .0106472    .2579616    .0506389    .0924616
---------+--------------------------------------------------------------------
    diff |           -.0721264     .024143               -.1195439    -.024709
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.9875
H0: diff = 0                                     Degrees of freedom =      585

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0015         Pr(|T| > |t|) = 0.0029          Pr(T > t) = 0.9985

.          krls notaccept  persparty       if exelection==1 & lose==1 
Iteration =  1, Looloss: 51.9041   

Pointwise Derivatives                                   Number of obs =      285 
                                                        Lambda        =    47.05 
                                                        Tolerance     =     .285 
                                                        Sigma         =        1 
                                                        Eff. df       =    2.346 
                                                        R2            =    .0277 
                                                        Looloss       =    51.77

 notaccept |      Avg.       SE        t    P>|t|        P25       P50       P75   
>     
-----------+--------------------------------------------------------------------
 persparty |  .073713   .024239    3.041    0.003    .019279   .056759   .170249  
-----------+--------------------------------------------------------------------


.          krls notaccept  persparty $cvar if exelection==1 & lose==1 
Iteration =  1, Looloss: 50.10464  
Iteration =  2, Looloss: 49.23644  
Iteration =  3, Looloss: 48.04588  
Iteration =  4, Looloss: 46.44889  
Iteration =  5, Looloss: 44.36995  
Iteration =  6, Looloss: 41.78113  
Iteration =  7, Looloss: 38.73942  
Iteration =  8, Looloss: 35.38961  
Iteration =  9, Looloss: 31.92735  
Iteration = 10, Looloss: 28.55906  
Iteration = 11, Looloss: 25.48028  
Iteration = 12, Looloss: 22.85181  
Iteration = 13, Looloss: 20.76642  
Iteration = 14, Looloss: 19.23126  
Iteration = 15, Looloss: 18.18162  
Iteration = 16, Looloss: 17.51376  
Iteration = 17, Looloss: 17.11695  
Iteration = 18, Looloss: 16.89489  
Iteration = 19, Looloss: 16.77592  

Pointwise Derivatives                                      Number of obs =      281
>  
                                                           Lambda        =    .1104
>  
                                                           Tolerance     =     .281
>  
                                                           Sigma         =        9
>  
                                                           Eff. df       =    116.8
>  
                                                           R2            =    .9396
>  
                                                           Looloss       =    16.68

    notaccept |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
--------------+--------------------------------------------------------------------
    persparty |  .074491    .01676    4.445    0.000    -.01024    .00362   .063238
>   
    v2elintim | -.039775   .007758   -5.127    0.000   -.031177  -.003713   .007876
>   
        ivdem | -.005789   .046269   -0.125    0.901   -.046162   .004453   .077622
>   
           ld | -.012471   .004807   -2.594    0.010    -.01169  -.002467   .003994
>   
        *pres | -.004433   .009643   -0.460    0.646   -.010253  -.001557   .004083
>   
*majoritarian |  .051392   .013635    3.769    0.000   -.001569   .009921   .035842
>   
  l1v2x_clphy |  .036449    .05659    0.644    0.520   -.017541   .031687   .067078
>   
   l1v2caviol |  -.00268   .003577   -0.749    0.454   -.003544   .000807   .005859
>   
         time |  .000935     .0004    2.336    0.020   -.000172   .000326   .001179
>   
--------------+--------------------------------------------------------------------


.          krls notaccept  persparty $cvar l1polar if exelection==1 & lose==1 
Iteration =  1, Looloss: 50.19357  
Iteration =  2, Looloss: 49.35133  
Iteration =  3, Looloss: 48.19289  
Iteration =  4, Looloss: 46.63986  
Iteration =  5, Looloss: 44.62206  
Iteration =  6, Looloss: 42.11081  
Iteration =  7, Looloss: 39.15584  
Iteration =  8, Looloss: 35.89683  
Iteration =  9, Looloss: 32.53288  
Iteration = 10, Looloss: 29.27348  
Iteration = 11, Looloss: 26.30784  
Iteration = 12, Looloss: 23.79058  
Iteration = 13, Looloss: 21.822    
Iteration = 14, Looloss: 20.42655  
Iteration = 15, Looloss: 19.54894  
Iteration = 16, Looloss: 19.07594  

Pointwise Derivatives                                        Number of obs =      2
> 81 
                                                             Lambda        =    .14
> 46 
                                                             Tolerance     =     .2
> 81 
                                                             Sigma         =       
> 10 
                                                             Eff. df       =    117
> .8 
                                                             R2            =    .93
> 55 
                                                             Looloss       =    18.
> 82

      notaccept |      Avg.       SE        t    P>|t|        P25       P50       P
> 75       
----------------+------------------------------------------------------------------
> --
      persparty |  .066357   .016942    3.917    0.000   -.010004   .004525   .0693
> 82  
      v2elintim | -.035114   .007378   -4.759    0.000   -.033296  -.001892   .0125
> 61  
          ivdem |   .01637   .042003    0.390    0.697    -.04414    .00182   .1002
> 13  
             ld |  -.01313   .004668   -2.813    0.005   -.013765  -.004095   .0041
> 49  
          *pres | -.001952   .009469   -0.206    0.837   -.012952  -.000266   .0062
> 44  
  *majoritarian |  .046986   .013819    3.400    0.001   -.002213   .008868   .0412
> 44  
    l1v2x_clphy |    .0667   .046468    1.435    0.152    .004394   .057522    .105
> 82  
     l1v2caviol | -.003295   .003689   -0.893    0.372   -.004102   .000723   .0052
> 43  
           time |  .000915    .00038    2.408    0.017   -.000228   .000377   .0013
> 69  
 l1polarization |  .004995   .003202    1.560    0.120   -.003629  -.000532   .0078
> 99  
----------------+------------------------------------------------------------------
> --


.          krls v2elaccept persparty $cvar if exelection==1 & lose==1
Iteration =  1, Looloss: 222.2272  
Iteration =  2, Looloss: 210.5595  
Iteration =  3, Looloss: 196.5027  
Iteration =  4, Looloss: 180.8888  
Iteration =  5, Looloss: 165.0008  
Iteration =  6, Looloss: 150.1995  
Iteration =  7, Looloss: 137.5308  
Iteration =  8, Looloss: 127.5598  
Iteration =  9, Looloss: 120.3909  
Iteration = 10, Looloss: 115.7557  
Iteration = 11, Looloss: 113.1624  

Pointwise Derivatives                                      Number of obs =      281
>  
                                                           Lambda        =    .8916
>  
                                                           Tolerance     =     .281
>  
                                                           Sigma         =        9
>  
                                                           Eff. df       =    54.01
>  
                                                           R2            =     .742
>  
                                                           Looloss       =    112.1

   v2elaccept |      Avg.       SE        t    P>|t|        P25       P50       P75
>        
--------------+--------------------------------------------------------------------
    persparty | -.230448    .12777   -1.804    0.072   -.544383  -.236571   .071153
>   
    v2elintim |   .22589   .043269    5.221    0.000    .107563   .191905   .315427
>   
        ivdem |   1.0304   .253501    4.065    0.000    .462646     1.002   1.51396
>   
           ld |  .033676   .033064    1.018    0.309   -.030303   .024788   .079241
>   
        *pres |  .002467   .075212    0.033    0.974   -.144122  -.015189   .118583
>   
*majoritarian | -.164341   .101422   -1.620    0.106   -.308721  -.043195   .091811
>   
  l1v2x_clphy | -.263499   .251532   -1.048    0.296   -.602852  -.105034   .216775
>   
   l1v2caviol |  .051938   .024614    2.110    0.036    .003649   .043493   .086755
>   
         time |  -.00027   .002908   -0.093    0.926   -.008824   .001365   .010774
>   
--------------+--------------------------------------------------------------------


. 
.         *********************************************************** 
.         *** Ruling Party personalism increases violent rhetoric ***
.         *********************************************************** 
.         twoway lpolyci v2paviol persparty

.         reg v2paviol persparty,cluster(lid)

Linear regression                               Number of obs     =        542
                                                F(1, 395)         =      38.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1018
                                                Root MSE          =     1.1746

                                  (Std. err. adjusted for 396 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -1.825982   .2946033    -6.20   0.000    -2.405169   -1.246796
       _cons |   1.644634   .1451733    11.33   0.000     1.359225    1.930043
------------------------------------------------------------------------------

.         reghdfe v2paviol persparty,a(year)cluster(lid)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        542
Absorbing 1 HDFE group                            F(   1,    395) =      40.61
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.1555
                                                  Adj R-squared   =     0.1060
                                                  Within R-sq.    =     0.1078
Number of clusters (lid)     =        396         Root MSE        =     1.1708

                                  (Std. err. adjusted for 396 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -1.869084   .2933045    -6.37   0.000    -2.445717    -1.29245
       _cons |   1.666144   .1446882    11.52   0.000     1.381689    1.950599
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        year |        30           0          30     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty,a(cowcode)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 1 iterations)

HDFE Linear regression                            Number of obs   =        535
Absorbing 1 HDFE group                            F(   1,    388) =       6.39
Statistics robust to heteroskedasticity           Prob > F        =     0.0119
                                                  R-squared       =     0.7753
                                                  Adj R-squared   =     0.7304
                                                  Within R-sq.    =     0.0242
Number of clusters (lid)     =        389         Root MSE        =     0.6407

                                  (Std. err. adjusted for 389 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.7070598   .2797288    -2.53   0.012    -1.257034   -.1570859
       _cons |   1.101384   .1349905     8.16   0.000     .8359799    1.366789
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        89           0          89     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty,a(cowcode year)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        535
Absorbing 2 HDFE groups                           F(   1,    388) =       7.77
Statistics robust to heteroskedasticity           Prob > F        =     0.0056
                                                  R-squared       =     0.7926
                                                  Adj R-squared   =     0.7338
                                                  Within R-sq.    =     0.0308
Number of clusters (lid)     =        389         Root MSE        =     0.6365

                                  (Std. err. adjusted for 389 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.7981901   .2863654    -2.79   0.006    -1.361212    -.235168
       _cons |   1.146675   .1370388     8.37   0.000     .8772433    1.416106
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        89           0          89     |
        year |        30           1          29     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty ld ivdem,a(cowcode year)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        535
Absorbing 2 HDFE groups                           F(   3,    388) =       7.06
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.8108
                                                  Adj R-squared   =     0.7560
                                                  Within R-sq.    =     0.1157
Number of clusters (lid)     =        389         Root MSE        =     0.6095

                                  (Std. err. adjusted for 389 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |   -.699529   .2531463    -2.76   0.006    -1.197239   -.2018189
          ld |  -.0582288    .117353    -0.50   0.620    -.2889562    .1724986
       ivdem |   3.414682   .9753776     3.50   0.001     1.496996    5.332369
       _cons |  -1.231482   .7305944    -1.69   0.093    -2.667902    .2049372
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        89           0          89     |
        year |        30           1          29     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty ld ivdem i_pop,a(cowcode year)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        535
Absorbing 2 HDFE groups                           F(   4,    388) =       9.76
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8225
                                                  Adj R-squared   =     0.7705
                                                  Within R-sq.    =     0.1704
Number of clusters (lid)     =        389         Root MSE        =     0.5911

                                  (Std. err. adjusted for 389 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.5198986   .2499016    -2.08   0.038    -1.011229   -.0285678
          ld |  -.0050373   .1168059    -0.04   0.966    -.2346891    .2246144
       ivdem |   3.314187   .9400451     3.53   0.000     1.465968    5.162407
  i_populism |  -.8752352   .2199778    -3.98   0.000    -1.307733   -.4427376
       _cons |  -1.105708   .7096074    -1.56   0.120    -2.500865    .2894488
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        89           0          89     |
        year |        30           1          29     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty ld ivdem ipi,a(cowcode year)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        518
Absorbing 2 HDFE groups                           F(   4,    375) =       7.51
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8121
                                                  Adj R-squared   =     0.7559
                                                  Within R-sq.    =     0.1162
Number of clusters (lid)     =        376         Root MSE        =     0.5976

                                  (Std. err. adjusted for 376 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.6956108   .2539792    -2.74   0.006    -1.195013    -.196209
          ld |  -.1335347   .1115923    -1.20   0.232    -.3529597    .0858904
       ivdem |   3.210925   1.078944     2.98   0.003     1.089385    5.332464
         ipi |   .9042799   1.043243     0.87   0.387     -1.14706     2.95562
       _cons |  -1.494243   .6780135    -2.20   0.028    -2.827427   -.1610576
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        87           0          87     |
        year |        30           1          29     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty ld ivdem ipolar,a(cowcode year)cluster(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        511
Absorbing 2 HDFE groups                           F(   4,    371) =       6.95
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8232
                                                  Adj R-squared   =     0.7694
                                                  Within R-sq.    =     0.1369
Number of clusters (lid)     =        372         Root MSE        =     0.5960

                                  (Std. err. adjusted for 372 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |   -.716886   .2506327    -2.86   0.004    -1.209725   -.2240471
          ld |  -.1752067   .1063558    -1.65   0.100    -.3843426    .0339291
       ivdem |   2.998086   .9679602     3.10   0.002      1.09471    4.901462
      ipolar |  -.2059924   .0943447    -2.18   0.030    -.3915097    -.020475
       _cons |  -.6592669   .7642101    -0.86   0.389    -2.161994    .8434596
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        87           0          87     |
        year |        30           1          29     |
-----------------------------------------------------+

.         reghdfe v2paviol persparty ld ivdem i_pop ipi ipolar,a(cowcode year)clust
> er(lid)
(dropped 7 singleton observations)
(MWFE estimator converged in 10 iterations)

HDFE Linear regression                            Number of obs   =        508
Absorbing 2 HDFE groups                           F(   6,    369) =       8.88
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.8286
                                                  Adj R-squared   =     0.7755
                                                  Within R-sq.    =     0.1958
Number of clusters (lid)     =        370         Root MSE        =     0.5775

                                  (Std. err. adjusted for 370 clusters in lid)
------------------------------------------------------------------------------
             |               Robust
    v2paviol | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   persparty |  -.5431353   .2483953    -2.19   0.029    -1.031583   -.0546873
          ld |  -.1571048   .1112305    -1.41   0.159    -.3758299    .0616203
       ivdem |     2.6734    1.00456     2.66   0.008     .6980206     4.64878
  i_populism |  -.8271138   .2186838    -3.78   0.000    -1.257137   -.3970909
         ipi |   1.152775   .9763032     1.18   0.238    -.7670406    3.072591
      ipolar |   -.229951   .0934286    -2.46   0.014    -.4136703   -.0462317
       _cons |  -1.118299   .6898115    -1.62   0.106    -2.474754    .2381559
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     cowcode |        86           0          86     |
        year |        30           1          29     |
-----------------------------------------------------+

.                         
. 
.         
.         
. *******************************************************************
. *********** EVS data on justify political violence ****************
. *******************************************************************     
. use "$dir\ZA7500_v4-0-0.dta",clear

. 
. tab c_abrv  if country==643 | country==112 | country==31 | country==51

    country |
abbreviatio |
     n (ISO |
     3166-1 |
    Alpha-2 |
     code)  |      Freq.     Percent        Cum.
------------+-----------------------------------
         AM |      1,500       22.48       22.48
         AZ |      1,800       26.97       49.45
         BY |      1,548       23.20       72.65
         RU |      1,825       27.35      100.00
------------+-----------------------------------
      Total |      6,673      100.00

. drop if   country==643 | country==112 | country==31 | country==51
(6,673 observations deleted)

. egen panel = group(country year mode)

. egen local = group(country year mode v275b_N2)

. 
. recode v100 (3=0) (2=.5) (1=1) (-1 -2=0),gen(protest)
(39,939 differences between v100 and protest)

. recode v211 (5=0) (4=.25) (3=.5) (2=.75) (1=1) (-2 -1 0 =.),gen(socialmedia)
(42,960 differences between v211 and socialmedia)

. recode v210 (5=0) (4=.25) (3=.5) (2=.75) (1=1) (-2 -1 0 =.),gen(newspaper)
(41,641 differences between v210 and newspaper)

. recode v209 (5=0) (4=.25) (3=.5) (2=.75) (1=1) (-2 -1 0 =.),gen(radio)
(41,455 differences between v209 and radio)

. recode v208 (5=0) (4=.25) (3=.5) (2=.75) (1=1) (-2 -1 0 =.),gen(tv)
(32,784 differences between v208 and tv)

. recode v162 (-2 -1 0 =.) , gen(polviolence)
(1,010 differences between v162 and polviolence)

. recode v148  (4=0) (3=.333) (2=.667) (1=1) (-2 -1 0 =.) , gen(demsupport)
(20,794 differences between v148 and demsupport)

. recode v145  (4=0) (3=.333) (2=.667) (1=1) (-2 -1 0 =.) , gen(strongmansupport)
(44,943 differences between v145 and strongmansupport)

. recode v172 (1=1) (2=.5) (-1 -2 3=0) (7=.),gen(vote)
(17,533 differences between v172 and vote)

. 
. gen emp_fulltime = v244==1 if v244~=.

. gen emp_retired = v244==5  if v244~=.

. gen emp_unemp = v244==8  if v244~=.

. gen xage = age if age>=18
(325 missing values generated)

. gen native = v227==1

. gen educ_med=v243_r==2

. gen educ_high=v243_r==3

. gen income_med=v261_r==2

. gen incom_high=v261_r==3

. gen female = v225==2

. gen rightid = v102>=6 & v102<=10

. gen leftid =v102>=1 & v102<=4

. gen noconfidencepolparties = v130==3 | v130==4

. 
.   label copy V174_CS mylabel2

.   gen pid = v174_cs

.   label value pid mylabel2

.   egen tag = tag(cntry_y pid)

.   gen pid2 = pid

.   sort c_abrv

.   browse pid pid2 c_abrv year if tag==1

.   
.   **********************************************
.   *** Get Varieties of Parties partyid codes ***
.   **********************************************
.   gen v2paid=.
(49,818 missing values generated)

.   qui do vparties-evs-codes.do

.   sum year

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
        year |     49,818    2017.623    .7317885       2017       2020

.   sort v2paid year

.   save temp,replace
(file temp.dta not found)
file temp.dta saved

. 
. use "$dir\pers-use.dta",clear

. tsset cowcode year

Panel variable: cowcode (unbalanced)
 Time variable: year, 1991 to 2020, but with gaps
         Delta: 1 unit

. gen incv2paid = v2paid
(55 missing values generated)

. gen jan1persparty =persparty

. gen dec31persparty = f.persparty 
(132 missing values generated)

. gen jan1rulingpers = v2paind_ord==3 | v2paind_ord==4 if v2paind_ord~=.
(149 missing values generated)

. replace v2paind = f.v2paind if v2eltvrexo==2
(216 real changes made, 14 to missing)

. rename v2paind rulingv2paind

.         ** Attacks on the State **
.                 gen ojud = l1v2x_jucon if year==min
(1,815 missing values generated)

.                 egen ijud = max(ojud),by(lid)
(55 missing values generated)

.                  alpha v2jupurge v2jupoatck v2jupack,item std gen(attack)

Test scale = mean(standardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         |  Obs  Sign   correlation   correlation     correlation     alpha
-------------+-----------------------------------------------------------------
v2jupurge    | 2392    +       0.8549        0.6559          0.4472      0.6180
v2jupoatck   | 2392    +       0.8209        0.5895          0.5314      0.6940
v2jupack     | 2392    +       0.7994        0.5497          0.5845      0.7378
-------------+-----------------------------------------------------------------
Test scale   |                                               0.5210      0.7654
-------------------------------------------------------------------------------

.                  replace attack = attack*-1
(2,392 real changes made)

.                  qui sum attack

.                  replace attack = (attack +abs(r(min)))/(r(max) + abs(r(min)))
(2,392 real changes made)

.                  sum attack

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
      attack |      2,392    .2656246    .1535195          0          1

.                  hist attack
(bin=33, start=0, width=.03030303)

.                  tsset lid year

Panel variable: lid (unbalanced)
 Time variable: year, 1991 to 2020
         Delta: 1 unit

.                  tssmooth ma l12attacks = attack,window(2 0 0)
The smoother applied was
     by lid : (1/2)*[x(t-2) + x(t-1) + 0*x(t)]; x(t)= attack

.         ** Accept election outcome **
.                  tsset lid year

Panel variable: lid (unbalanced)
 Time variable: year, 1991 to 2020
         Delta: 1 unit

.                  gen accept = v2elaccept
(1,647 missing values generated)

.                  replace accept = l1.v2elaccept if accept==.
(294 real changes made)

.                  replace accept = l2.v2elaccept if accept==.
(176 real changes made)

.                  replace accept = l3.v2elaccept if accept==.
(95 real changes made)

.                  replace accept = l4.v2elaccept if accept==.
(31 real changes made)

. sort cowcode year

. save pers-temp,replace
(file pers-temp.dta not found)
file pers-temp.dta saved

. 
. use "$dir\vdem-parties-merge.dta",clear
(V-Dem CPD)

. keep if year>=2017 & year<=2020
(92,053 observations deleted)

. merge v2paid year using temp
(you are using old merge syntax; see [D] merge for new syntax)
variables v2paid year do not uniquely identify observations in temp.dta

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |      5,276        9.58        9.58
          2 |     17,645       32.03       41.60
          3 |     32,173       58.40      100.00
------------+-----------------------------------
      Total |     55,094      100.00

. drop _merge

. rename COW cowcode

. sort cowcode year

. merge cowcode year using pers-temp
(you are using old merge syntax; see [D] merge for new syntax)
variables cowcode year do not uniquely identify observations in the master data
(note: variable country was str45 in the using data, but will be int now)
(variable year was float, now double to accommodate using data's values)
(variable cowcode was long, now double to accommodate using data's values)
(variable v1 was byte, now int to accommodate using data's values)
(label paactcom_ord already defined)
(label paanteli_ord already defined)
(label paclient_ord already defined)
(label paculsup_ord already defined)
(label padisa_ord already defined)
(label pagender_ord already defined)
(label paimmig_ord already defined)
(label paind_ord already defined)
(label palgbt_ord already defined)
(label palocoff_ord already defined)
(label paminor_ord already defined)
(label panom_ord already defined)
(label paopresp_ord already defined)
(label papariah_ord already defined)
(label papeople_ord already defined)
(label paplur_ord already defined)
(label parelig_ord already defined)
(label pariglef_ord already defined)
(label pasoctie_ord already defined)
(label paviol_ord already defined)
(label pawelf_ord already defined)
(label pawomlab_ord already defined)
(label regiongeo already defined)
(label regionpol already defined)
(label regionpol_6C already defined)
(label storical already defined)
(label oject already defined)
(label paallian already defined)
(label paelcont already defined)
(label pagovsup already defined)

. tab _merge

     _merge |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     23,050       40.35       40.35
          2 |      2,032        3.56       43.91
          3 |     32,044       56.09      100.00
------------+-----------------------------------
      Total |     57,126      100.00

. tab country if _merge==1

     country code (ISO |
 3166-1 numeric code)  |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
               Albania |        516        2.45        2.45
               Austria |        389        1.85        4.30
Bosnia and Herzegovina |      1,480        7.03       11.33
              Bulgaria |        595        2.83       14.16
               Croatia |        627        2.98       17.13
               Czechia |        503        2.39       19.52
               Denmark |        471        2.24       21.76
               Estonia |        462        2.19       23.95
               Finland |        211        1.00       24.96
                France |        775        3.68       28.64
               Georgia |        987        4.69       33.33
               Germany |        481        2.28       35.61
               Hungary |        623        2.96       38.57
               Iceland |        318        1.51       40.08
                 Italy |      1,157        5.50       45.58
             Lithuania |        640        3.04       48.62
            Montenegro |      1,003        4.76       53.38
           Netherlands |        669        3.18       56.56
                Norway |        186        0.88       57.44
                Poland |        560        2.66       60.10
              Portugal |        671        3.19       63.29
               Romania |        857        4.07       67.36
                Serbia |        748        3.55       70.91
              Slovakia |        525        2.49       73.41
              Slovenia |        555        2.64       76.05
                 Spain |        580        2.76       78.80
                Sweden |        240        1.14       79.94
           Switzerland |      3,174       15.08       95.02
       North Macedonia |        638        3.03       98.05
         Great Britain |        411        1.95      100.00
-----------------------+-----------------------------------
                 Total |     21,052      100.00

. tab country if persparty==. | polviolence==.

     country code (ISO |
 3166-1 numeric code)  |      Freq.     Percent        Cum.
-----------------------+-----------------------------------
               Albania |        523        2.44        2.44
               Austria |        405        1.89        4.33
Bosnia and Herzegovina |      1,480        6.91       11.24
              Bulgaria |        623        2.91       14.15
               Croatia |        631        2.94       17.09
               Czechia |        536        2.50       19.59
               Denmark |        481        2.24       21.84
               Estonia |        479        2.24       24.07
               Finland |        219        1.02       25.09
                France |        798        3.72       28.82
               Georgia |        995        4.64       33.46
               Germany |        497        2.32       35.78
               Hungary |        632        2.95       38.73
               Iceland |        335        1.56       40.29
                 Italy |      1,164        5.43       45.73
             Lithuania |        678        3.16       48.89
            Montenegro |      1,003        4.68       53.57
           Netherlands |        695        3.24       56.81
                Norway |        190        0.89       57.70
                Poland |        580        2.71       60.41
              Portugal |        672        3.14       63.54
               Romania |        865        4.04       67.58
                Serbia |        756        3.53       71.11
              Slovakia |        544        2.54       73.65
              Slovenia |        558        2.60       76.25
                 Spain |        593        2.77       79.02
                Sweden |        256        1.19       80.21
           Switzerland |      3,174       14.81       95.03
       North Macedonia |        648        3.02       98.05
         Great Britain |        418        1.95      100.00
-----------------------+-----------------------------------
                 Total |     21,428      100.00

. keep if persparty~=. & polviolence~=.  
(28,736 observations deleted)

.         
.  ********************************************************
.   egen group=group(local v2paid)

.   gen tempseatshare =v2paseat
(265 missing values generated)

.   gen z = v2panoallian/v2panumbseat if  v2panoallian~=. & v2panumbseat~=. 
(24,388 missing values generated)

.   sum z

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
           z |      4,002    1.556477    2.507041          1         66

.   replace tempseat = z if z~=. & z>v2paseat
(122 real changes made)

.   egen maxseat = max(tempseat),by(country) 

.   gen loser =tempseat<maxseat if tempseat~=. & maxseat~=.
(265 missing values generated)

.   tab loser

      loser |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      9,800       34.84       34.84
          1 |     18,325       65.16      100.00
------------+-----------------------------------
      Total |     28,125      100.00

.   qui sum v2paind

.   replace v2paind = (v2paind+abs(r(min)))/(abs(r(min))+r(max))
(28,124 real changes made)

.   tab v2paind_ord

  Personalization of |
               party |      Freq.     Percent        Cum.
---------------------+-----------------------------------
         Not focused |      6,466       22.99       22.99
Occasionally focused |      6,628       23.57       46.56
    Somewhat focused |      6,322       22.48       69.04
      Mainly focused |      5,346       19.01       88.05
      Solely focused |      3,362       11.95      100.00
---------------------+-----------------------------------
               Total |     28,124      100.00

.   gen personalist = v2paind_ord==3 | v2paind_ord==4 if v2paind_ord~=.
(266 missing values generated)

.   gen antielite = v2paanteli_ord==3 | v2paanteli_ord==4 if v2paanteli_ord~=.
(266 missing values generated)

.   gen people = v2papeople_ord==3 | v2papeople_ord==4 if v2papeople_ord~=.
(266 missing values generated)

.   replace xage = xage*.1
(28,272 real changes made)

.   recode v176 (-10 -9 -8 -7 -4 -2 -1 0=.),gen(unfairvotecount)
(2,651 differences between v176 and unfairvotecount)

.   tab v176 unfairvote,m

         how often in |
 country's elections: |   RECODE of v176 (how often in country's
    votes are counted |  elections: votes are counted fairly (Q50A
        fairly (Q50A) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
    item not included |         0          0          0          0 |     1,475 
            no answer |         0          0          0          0 |       103 
            dont know |         0          0          0          0 |     1,073 
           very often |    13,652          0          0          0 |    13,652 
         fairly often |         0      7,353          0          0 |     7,353 
            not often |         0          0      3,220          0 |     3,220 
     not at all often |         0          0          0      1,514 |     1,514 
----------------------+--------------------------------------------+----------
                Total |    13,652      7,353      3,220      1,514 |    28,390 


                      | RECODE of
                      | v176 (how
                      |  often in
                      | country's
                      | elections:
                      | votes are
         how often in |  counted
 country's elections: |   fairly
    votes are counted |   (Q50A
        fairly (Q50A) |         . |     Total
----------------------+-----------+----------
    item not included |     1,475 |     1,475 
            no answer |       103 |       103 
            dont know |     1,073 |     1,073 
           very often |         0 |    13,652 
         fairly often |         0 |     7,353 
            not often |         0 |     3,220 
     not at all often |         0 |     1,514 
----------------------+-----------+----------
                Total |     2,651 |    28,390 

.   recode v181 (-10 -9 -8 -7 -4 -2 -1 0=.),gen(unfairelection)
(3,227 differences between v181 and unfairelection)

.   tab v181 unfairelection,m

         how often in |
 country's elections: |   RECODE of v181 (how often in country's
   election officials |  elections: election officials are fair (Q
      are fair (Q50F) |         1          2          3          4 |     Total
----------------------+--------------------------------------------+----------
multiple answers Mail |         0          0          0          0 |         1 
    item not included |         0          0          0          0 |     1,475 
            no answer |         0          0          0          0 |       166 
            dont know |         0          0          0          0 |     1,585 
           very often |     9,926          0          0          0 |     9,926 
         fairly often |         0      9,561          0          0 |     9,561 
            not often |         0          0      4,171          0 |     4,171 
     not at all often |         0          0          0      1,505 |     1,505 
----------------------+--------------------------------------------+----------
                Total |     9,926      9,561      4,171      1,505 |    28,390 


                      | RECODE of
                      | v181 (how
                      |  often in
                      | country's
                      | elections:
                      |  election
         how often in | officials
 country's elections: |  are fair
   election officials |     (Q
      are fair (Q50F) |         . |     Total
----------------------+-----------+----------
multiple answers Mail |         1 |         1 
    item not included |     1,475 |     1,475 
            no answer |       166 |       166 
            dont know |     1,585 |     1,585 
           very often |         0 |     9,926 
         fairly often |         0 |     9,561 
            not often |         0 |     4,171 
     not at all often |         0 |     1,505 
----------------------+-----------+----------
                Total |     3,227 |    28,390 

.   gen anyviolence = polviolence>=5 if polviolence~=.

.   tab polviolence anyviolence,m

 RECODE of |
  v162 (do |
       you |
  justify: |
 political |
  violence |      anyviolence
   (Q44N)) |         0          1 |     Total
-----------+----------------------+----------
         1 |    22,174          0 |    22,174 
         2 |     2,595          0 |     2,595 
         3 |     1,217          0 |     1,217 
         4 |       541          0 |       541 
         5 |         0        794 |       794 
         6 |         0        369 |       369 
         7 |         0        232 |       232 
         8 |         0        159 |       159 
         9 |         0        106 |       106 
        10 |         0        203 |       203 
-----------+----------------------+----------
     Total |    26,527      1,863 |    28,390 

.   
. *********************************************************** 
.   qui centile persparty  

.   local cut = r(c_1)

.   global cut = `cut'

.   global dvar = "xage female educ* emp* incom*"

.   drop b

.   
.     * No evidence that personalist voters justify violence more *
.   ttest anyviolence,by(personalist)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |  19,416      .06541    .0017745    .2472542    .0619319     .068888
       1 |   8,708    .0670648    .0026806    .2501485    .0618101    .0723195
---------+--------------------------------------------------------------------
Combined |  28,124    .0659223    .0014797    .2481507     .063022    .0688226
---------+--------------------------------------------------------------------
    diff |           -.0016548    .0032005                -.007928    .0046184
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.5170
H0: diff = 0                                     Degrees of freedom =    28122

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3026         Pr(|T| > |t|) = 0.6051          Pr(T > t) = 0.6974

.   qui melogit anyviolence personalist||country:

.         lincom personalist

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0416215    .062502    -0.67   0.505    -.1641232    .0808801
------------------------------------------------------------------------------

.   qui melogit anyviolence personalist $dvar||country:

.         lincom personalist

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0187388   .0631375    -0.30   0.767    -.1424861    .1050084
------------------------------------------------------------------------------

.   qui mixed anyviolence personalist||country:

.         lincom personalist

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0028151   .0040752    -0.69   0.490    -.0108023     .005172
------------------------------------------------------------------------------

.   qui mixed anyviolence personalist $dvar||country:

.         lincom personalist

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0014004   .0040826    -0.34   0.732     -.009402    .0066013
------------------------------------------------------------------------------

.         
. * Losers more likely to justify violence *
.         mixed anyviolence loser ||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -281.28651  
Iteration 1:  Log likelihood = -281.28651  

Computing standard errors ...

Mixed-effects ML regression                         Number of obs    =  28,125
Group variable: country                             Number of groups =      28
                                                    Obs per group:
                                                                 min =     244
                                                                 avg = 1,004.5
                                                                 max =   2,881
                                                    Wald chi2(1)     =   12.71
Log likelihood = -281.28651                         Prob > chi2      =  0.0004

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
       loser |   .0113764   .0031915     3.56   0.000     .0051212    .0176316
       _cons |   .0616593   .0091017     6.77   0.000     .0438204    .0794983
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0021304   .0005885      .0012397    .0036609
-----------------------------+------------------------------------------------
               var(Residual) |   .0595243   .0005022      .0585482    .0605167
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 840.90        Prob >= chibar2 = 0.0000

.         mixed anyviolence $dvar loser ||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -156.89603  
Iteration 1:  Log likelihood = -156.89603  

Computing standard errors ...

Mixed-effects ML regression                         Number of obs    =  28,008
Group variable: country                             Number of groups =      28
                                                    Obs per group:
                                                                 min =     244
                                                                 avg = 1,000.3
                                                                 max =   2,879
                                                    Wald chi2(10)    =  180.28
Log likelihood = -156.89603                         Prob > chi2      =  0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
        xage |  -.0074004   .0011943    -6.20   0.000    -.0097411   -.0050596
      female |  -.0178318   .0029562    -6.03   0.000    -.0236259   -.0120377
    educ_med |  -.0105421   .0040505    -2.60   0.009    -.0184808   -.0026033
   educ_high |  -.0144659   .0043819    -3.30   0.001    -.0230543   -.0058775
emp_fulltime |   .0019878   .0039846     0.50   0.618    -.0058218    .0097974
 emp_retired |  -.0095426   .0052683    -1.81   0.070    -.0198684    .0007831
   emp_unemp |  -.0055429   .0065737    -0.84   0.399    -.0184273    .0073414
  income_med |  -.0089664   .0036439    -2.46   0.014    -.0161082   -.0018245
  incom_high |  -.0194598   .0038973    -4.99   0.000    -.0270984   -.0118212
       loser |   .0090597   .0031957     2.83   0.005     .0027962    .0153232
       _cons |   .1312526   .0116407    11.28   0.000     .1084373    .1540678
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0021603   .0005967      .0012572    .0037122
-----------------------------+------------------------------------------------
               var(Residual) |    .059001   .0004988      .0580313    .0599868
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 840.87        Prob >= chibar2 = 0.0000

. 
.  * Average personalist voter effect on political violence is zero but positive ef
> fect for losing election *
.   mixed anyviolence personalist loser $dvar||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -157.01987  
Iteration 1:  Log likelihood = -157.01987  

Computing standard errors ...

Mixed-effects ML regression                         Number of obs    =  28,007
Group variable: country                             Number of groups =      28
                                                    Obs per group:
                                                                 min =     244
                                                                 avg = 1,000.2
                                                                 max =   2,879
                                                    Wald chi2(11)    =  181.01
Log likelihood = -157.01987                         Prob > chi2      =  0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0036661   .0044332     0.83   0.408    -.0050228     .012355
       loser |   .0101686    .003471     2.93   0.003     .0033655    .0169717
        xage |  -.0074233   .0011946    -6.21   0.000    -.0097646   -.0050819
      female |  -.0177982   .0029567    -6.02   0.000    -.0235933    -.012003
    educ_med |  -.0104849   .0040514    -2.59   0.010    -.0184255   -.0025443
   educ_high |  -.0142905   .0043874    -3.26   0.001    -.0228896   -.0056913
emp_fulltime |   .0019625   .0039847     0.49   0.622    -.0058474    .0097724
 emp_retired |  -.0095226   .0052685    -1.81   0.071    -.0198487    .0008034
   emp_unemp |  -.0055733   .0065742    -0.85   0.397    -.0184584    .0073118
  income_med |  -.0089624    .003644    -2.46   0.014    -.0161045   -.0018203
  incom_high |  -.0194672   .0038975    -4.99   0.000    -.0271061   -.0118283
       _cons |   .1292804    .011908    10.86   0.000     .1059411    .1526197
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0021705   .0005998      .0012629    .0037305
-----------------------------+------------------------------------------------
               var(Residual) |   .0590012   .0004988      .0580316    .0599871
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 839.60        Prob >= chibar2 = 0.0000

.         est store v1  

.  * Now look at whether personalist voters are more likely than nonpersonalist vot
> ers to endorse violence when they lose **
.         xtsum anyviolence personalist  if lose==0,i(country)  /* no within variat
> ion in personalist voter among winners */
warning: existing panel variable is not country

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
anyvio~e overall |  .0585714   .2348328          0          1 |     N =    9800
         between |             .0489072          0   .1858407 |     n =      28
         within  |             .2312591  -.1272693   1.042894 | T-bar =     350
                 |                                            |
person~t overall |  .5160731   .4997671          0          1 |     N =    9799
         between |             .5091751          0          1 |     n =      27
         within  |                    0   .5160731   .5160731 | T-bar = 362.926

.         reg anyviolence personalist $dvar if lose==0          /* pooled effect is
>  0.6 percent */

      Source |       SS           df       MS      Number of obs   =     9,750
-------------+----------------------------------   F(10, 9739)     =      4.27
       Model |  2.34312479        10  .234312479   Prob > F        =    0.0000
    Residual |  534.333798     9,739  .054865366   R-squared       =    0.0044
-------------+----------------------------------   Adj R-squared   =    0.0033
       Total |  536.676923     9,749  .055049433   Root MSE        =    .23423

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0067253   .0048324     1.39   0.164    -.0027472    .0161979
        xage |   -.005161   .0019547    -2.64   0.008    -.0089927   -.0013294
      female |  -.0117266    .004814    -2.44   0.015     -.021163   -.0022902
    educ_med |  -.0097406   .0062115    -1.57   0.117    -.0219164    .0024353
   educ_high |  -.0130863   .0070594    -1.85   0.064    -.0269241    .0007515
emp_fulltime |  -.0098142   .0066601    -1.47   0.141    -.0228694    .0032409
 emp_retired |  -.0180255   .0084255    -2.14   0.032    -.0345412   -.0015099
   emp_unemp |  -.0193412   .0102828    -1.88   0.060    -.0394975    .0008152
  income_med |  -.0087719    .005879    -1.49   0.136    -.0202961    .0027522
  incom_high |  -.0151324   .0063228    -2.39   0.017    -.0275265   -.0027383
       _cons |   .1157507   .0121801     9.50   0.000     .0918751    .1396262
------------------------------------------------------------------------------

.         mixed anyviolence personalist $dvar if lose==0||country:  /* partial pool
> ed is 0.4 percent */

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood =  426.88952  
Iteration 1:  Log likelihood =  426.88952  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  9,750
Group variable: country                              Number of groups =     27
                                                     Obs per group:
                                                                  min =     49
                                                                  avg =  361.1
                                                                  max =    892
                                                     Wald chi2(10)    =  44.83
Log likelihood =  426.88952                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0042103   .0179315     0.23   0.814    -.0309347    .0393554
        xage |  -.0052851   .0019564    -2.70   0.007    -.0091195   -.0014507
      female |  -.0108931   .0047789    -2.28   0.023    -.0202596   -.0015267
    educ_med |  -.0094615   .0063977    -1.48   0.139    -.0220007    .0030777
   educ_high |  -.0092078   .0071829    -1.28   0.200     -.023286    .0048705
emp_fulltime |  -.0067162   .0066699    -1.01   0.314    -.0197889    .0063566
 emp_retired |  -.0201976   .0084482    -2.39   0.017    -.0367557   -.0036394
   emp_unemp |  -.0139595   .0105592    -1.32   0.186    -.0346552    .0067363
  income_med |  -.0085559   .0058916    -1.45   0.146    -.0201032    .0029914
  incom_high |   -.016085   .0063804    -2.52   0.012    -.0285905   -.0035796
       _cons |   .1235053   .0171697     7.19   0.000     .0898533    .1571572
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |    .001967   .0006022      .0010795    .0035845
-----------------------------+------------------------------------------------
               var(Residual) |   .0532643    .000764      .0517878    .0547829
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 209.06        Prob >= chibar2 = 0.0000

.         replace personalist= personalist*2
(8,708 real changes made)

.   mixed anyviolence personalist $dvar if lose==0||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood =  426.88952  
Iteration 1:  Log likelihood =  426.88952  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  9,750
Group variable: country                              Number of groups =     27
                                                     Obs per group:
                                                                  min =     49
                                                                  avg =  361.1
                                                                  max =    892
                                                     Wald chi2(10)    =  44.83
Log likelihood =  426.88952                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0021052   .0089657     0.23   0.814    -.0154674    .0196777
        xage |  -.0052851   .0019564    -2.70   0.007    -.0091195   -.0014507
      female |  -.0108931   .0047789    -2.28   0.023    -.0202596   -.0015267
    educ_med |  -.0094615   .0063977    -1.48   0.139    -.0220007    .0030777
   educ_high |  -.0092078   .0071829    -1.28   0.200     -.023286    .0048705
emp_fulltime |  -.0067162   .0066699    -1.01   0.314    -.0197889    .0063566
 emp_retired |  -.0201976   .0084482    -2.39   0.017    -.0367557   -.0036394
   emp_unemp |  -.0139595   .0105592    -1.32   0.186    -.0346552    .0067363
  income_med |  -.0085559   .0058916    -1.45   0.146    -.0201032    .0029914
  incom_high |   -.016085   .0063804    -2.52   0.012    -.0285905   -.0035796
       _cons |   .1235053   .0171697     7.19   0.000     .0898533    .1571572
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |    .001967   .0006022      .0010795    .0035845
-----------------------------+------------------------------------------------
               var(Residual) |   .0532643    .000764      .0517878    .0547829
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 209.06        Prob >= chibar2 = 0.0000

.         est store v2

.         recode personalist (2=1)
(8,708 changes made to personalist)

.   mixed anyviolence personalist $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -543.51411  
Iteration 1:  Log likelihood = -543.51411  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(10)    = 130.65
Log likelihood = -543.51411                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0247382   .0061949     3.99   0.000     .0125963      .03688
        xage |  -.0078531   .0015098    -5.20   0.000    -.0108123   -.0048939
      female |  -.0212617   .0037467    -5.67   0.000    -.0286051   -.0139183
    educ_med |  -.0100912   .0052039    -1.94   0.052    -.0202907    .0001083
   educ_high |  -.0145929   .0055483    -2.63   0.009    -.0254673   -.0037185
emp_fulltime |   .0052664   .0049656     1.06   0.289     -.004466    .0149988
 emp_retired |  -.0035195   .0067105    -0.52   0.600    -.0166717    .0096328
   emp_unemp |  -.0034726   .0083592    -0.42   0.678    -.0198564    .0129112
  income_med |  -.0080621   .0046144    -1.75   0.081    -.0171062     .000982
  incom_high |  -.0189411   .0049227    -3.85   0.000    -.0285894   -.0092929
       _cons |   .1328686   .0135533     9.80   0.000     .1063046    .1594326
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0027015   .0007588      .0015578    .0046848
-----------------------------+------------------------------------------------
               var(Residual) |   .0618341   .0006477      .0605776    .0631167
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 618.13        Prob >= chibar2 = 0.0000

.         est store v3

.   mixed anyviolence personalist v2xpa_popul $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -535.85802  
Iteration 1:  Log likelihood = -535.85802  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(11)    = 146.04
Log likelihood = -535.85802                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0144127   .0067136     2.15   0.032     .0012542    .0275711
 v2xpa_popul |   .0345592   .0088134     3.92   0.000     .0172853    .0518331
        xage |  -.0076471   .0015101    -5.06   0.000    -.0106069   -.0046873
      female |  -.0207302   .0037477    -5.53   0.000    -.0280755   -.0133848
    educ_med |   -.009807   .0052019    -1.89   0.059    -.0200026    .0003886
   educ_high |  -.0126946   .0055673    -2.28   0.023    -.0236062   -.0017829
emp_fulltime |   .0049963   .0049641     1.01   0.314    -.0047331    .0147257
 emp_retired |   -.003156   .0067084    -0.47   0.638    -.0163041    .0099922
   emp_unemp |  -.0039527    .008356    -0.47   0.636    -.0203301    .0124247
  income_med |   -.007766   .0046131    -1.68   0.092    -.0168075    .0012756
  incom_high |  -.0179054   .0049277    -3.63   0.000    -.0275635   -.0082472
       _cons |   .1203521    .013697     8.79   0.000     .0935065    .1471977
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0025254    .000712      .0014532    .0043886
-----------------------------+------------------------------------------------
               var(Residual) |   .0617883   .0006472      .0605328    .0630699
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 553.18        Prob >= chibar2 = 0.0000

.         est store v4

.         
.         label var personalist  `""{bf:Personalist}" "{bf:party}    " "{bf:voter} 
>   ""'  

.         label var loser "Election loser"

.         label var female "Female"

.         label var xage "Age"

.         label var leftid "Left self id"

.         label var rightid "Right self id"

.         label var educ_med `""Medium   " "education" "(low)   ""'

.         label var income_med `""Medium  " "income" "(low)  ""'

.         label var educ_high `""High   " "education" "(low)   ""'

.         label var incom_high `""High  " "income" "(low)  ""'

.         label var v2xpa_popul "Populism"

.         coefplot(v1, msymbol(O))(v2, msymbol(S))(v3, msymbol(T))  , ///
>                 drop(_cons emp_fulltime emp_retired emp_unemp) ///
>                 grid(glcolor(gs15))xline(0,lpattern(dash)) xlab(-.04(.02).04) ///
>                 xtitle(Coefficient estimates,size(small))order(loser personalist 
> $dvar loser)level(90 95) title("Justify political violence", ///
>                 size(medium)height(6))xsize(2) ysize(3.5)mlabel format(%9.2g) ///
>                 mlabsize(vsmall)mlabposition(11)mlabgap(*.75) ///
>                 legend(lab(3 "All voters")lab(6 "Election winners") lab(9 "Electi
> on losers")      ///
>                 order(3 6  9)size(vsmall)pos(6)col(4)ring(1)note(" " "Respondents
>  from 28 countries in the European Values Survey." ///
>                 "Constant and employment indictators included but not reported." 
> "Mixed effects linear model.",size(small)pos(6))) 

.         gr export "$dir\golden\Ch6-EVS-Justify-Violence.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h6-EVS-Justify-Violence.pdf saved as PDF format

. 
.         
.         coefplot(v1, msymbol(O))   , ///
>                 drop(_cons emp_fulltime emp_retired emp_unemp) ///
>                 grid(glcolor(gs15))xline(0,lpattern(dash)) xlab(-.04(.02).04) ///
>                 xtitle(Coefficient estimates,size(small))order(loser personalist 
> $dvar loser)level(90 95) title("Justify political violence", ///
>                 size(medium)height(6))xsize(2) ysize(3.5)mlabel format(%9.2g) ///
>                 mlabsize(vsmall)mlabposition(11)mlabgap(*.75) ///
>                 legend(lab(3 "All voters")lab(6 "Election winners") lab(9 "Electi
> on losers")      ///
>                 order(3 6  9)size(vsmall)pos(6)col(4)ring(1)note(" " "Respondents
>  from 28 countries in the European Values Survey." ///
>                 "Constant and employment indictators included but not reported." 
> "Mixed effects linear model.",size(small)pos(6))) 

.         gr export "$dir\golden\T-EVS-Justify-Violence1.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -EVS-Justify-Violence1.pdf saved as PDF format

.                 
.                 
.                 coefplot(v1, msymbol(O))(v2, msymbol(S))(v3, msymbol(T))  , ///
>                 drop(_cons $dvar loser emp_fulltime emp_retired emp_unemp) ///
>                 grid(glcolor(gs15))xline(0,lpattern(dash)) xlab(-.04(.02).04) ///
>                 xtitle(Coefficient estimates,size(small))order( personalist)level
> (90 95) title("Justify political violence", ///
>                 size(medium)height(6))xsize(2) ysize(3.5)mlabel format(%9.2g) ///
>                 mlabsize(vsmall)mlabposition(11)mlabgap(*.75) ///
>                 legend(lab(3 "All voters")lab(6 "Election winners") lab(9 "Electi
> on losers")      ///
>                 order(3 6  9)size(vsmall)pos(6)col(4)ring(1)note(" " "Respondents
>  from 28 countries in the European Values Survey." ///
>                 "Constant and employment indictators included but not reported." 
> "Mixed effects linear model.",size(small)pos(6))) 

.         gr export "$dir\golden\T-EVS-Justify-Violence2.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -EVS-Justify-Violence2.pdf saved as PDF format

.                 
. * Check with additional specifications *
.    mixed anyviolence personalist                      if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -630.08974  
Iteration 1:  Log likelihood = -630.08974  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,325
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  654.5
                                                                  max =  1,988
                                                     Wald chi2(1)     =  19.85
Log likelihood = -630.08974                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0275177   .0061768     4.46   0.000     .0154114    .0396239
       _cons |   .0646562   .0102262     6.32   0.000     .0446131    .0846992
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0027428   .0007684      .0015839    .0047497
-----------------------------+------------------------------------------------
               var(Residual) |   .0624076   .0006525      .0611418    .0636996
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 645.05        Prob >= chibar2 = 0.0000

.    mixed anyviolence personalist leftid rightid $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -543.43765  
Iteration 1:  Log likelihood = -543.43765  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(12)    = 130.80
Log likelihood = -543.43765                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0244305   .0062432     3.91   0.000      .012194     .036667
      leftid |  -.0015014   .0048875    -0.31   0.759    -.0110807    .0080779
     rightid |   .0001646   .0047739     0.03   0.972    -.0091921    .0095214
        xage |  -.0078524   .0015103    -5.20   0.000    -.0108126   -.0048922
      female |  -.0212344   .0037582    -5.65   0.000    -.0286004   -.0138684
    educ_med |  -.0100255   .0052094    -1.92   0.054    -.0202358    .0001847
   educ_high |  -.0144015   .0055822    -2.58   0.010    -.0253425   -.0034605
emp_fulltime |   .0052473   .0049661     1.06   0.291    -.0044861    .0149806
 emp_retired |  -.0035004   .0067107    -0.52   0.602    -.0166531    .0096523
   emp_unemp |  -.0034882   .0083611    -0.42   0.677    -.0198757    .0128993
  income_med |  -.0080413   .0046151    -1.74   0.081    -.0170868    .0010041
  incom_high |  -.0189724   .0049297    -3.85   0.000    -.0286344   -.0093104
       _cons |   .1332718   .0138092     9.65   0.000     .1062062    .1603374
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026946   .0007571      .0015536    .0046736
-----------------------------+------------------------------------------------
               var(Residual) |   .0618339   .0006477      .0605774    .0631164
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 615.07        Prob >= chibar2 = 0.0000

.    mixed anyviolence personalist native $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -541.43293  
Iteration 1:  Log likelihood = -541.43293  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(11)    = 134.84
Log likelihood = -541.43293                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |    .024615   .0061945     3.97   0.000      .012474    .0367559
      native |  -.0156516   .0076712    -2.04   0.041    -.0306869   -.0006163
        xage |   -.007848   .0015096    -5.20   0.000    -.0108069   -.0048892
      female |  -.0213896   .0037468    -5.71   0.000    -.0287332    -.014046
    educ_med |  -.0100509   .0052034    -1.93   0.053    -.0202493    .0001475
   educ_high |  -.0146966   .0055479    -2.65   0.008    -.0255702    -.003823
emp_fulltime |   .0052441    .004965     1.06   0.291    -.0044872    .0149754
 emp_retired |  -.0033213   .0067104    -0.49   0.621    -.0164735    .0098308
   emp_unemp |  -.0036297   .0083586    -0.43   0.664    -.0200124    .0127529
  income_med |  -.0078088   .0046156    -1.69   0.091    -.0168552    .0012376
  incom_high |  -.0185314   .0049262    -3.76   0.000    -.0281865   -.0088762
       _cons |   .1474381   .0153163     9.63   0.000     .1174187    .1774575
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026995   .0007582      .0015567    .0046811
-----------------------------+------------------------------------------------
               var(Residual) |   .0618201   .0006475      .0605639    .0631024
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 617.88        Prob >= chibar2 = 0.0000

.    mixed anyviolence personalist v2paminor $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -532.55257  
Iteration 1:  Log likelihood = -532.55257  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(11)    = 152.71
Log likelihood = -532.55257                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0142989   .0065737     2.18   0.030     .0014146    .0271832
   v2paminor |  -.0095929   .0020475    -4.69   0.000    -.0136059   -.0055798
        xage |  -.0078951    .001509    -5.23   0.000    -.0108526   -.0049377
      female |  -.0204453   .0037486    -5.45   0.000    -.0277923   -.0130982
    educ_med |  -.0102193   .0052007    -1.96   0.049    -.0204125   -.0000261
   educ_high |  -.0126779     .00556    -2.28   0.023    -.0235753   -.0017804
emp_fulltime |   .0048833   .0049633     0.98   0.325    -.0048447    .0146112
 emp_retired |  -.0033635   .0067066    -0.50   0.616    -.0165081    .0097811
   emp_unemp |  -.0034113    .008354    -0.41   0.683    -.0197848    .0129623
  income_med |  -.0079803   .0046117    -1.73   0.084    -.0170191    .0010584
  incom_high |  -.0182746   .0049217    -3.71   0.000    -.0279211   -.0086282
       _cons |   .1458111   .0137075    10.64   0.000     .1189449    .1726773
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026095   .0007339      .0015037    .0045286
-----------------------------+------------------------------------------------
               var(Residual) |    .061763   .0006469      .0605079     .063044
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 601.27        Prob >= chibar2 = 0.0000

.    mixed anyviolence personalist v2xpa_popul $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -535.85802  
Iteration 1:  Log likelihood = -535.85802  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(11)    = 146.04
Log likelihood = -535.85802                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0144127   .0067136     2.15   0.032     .0012542    .0275711
 v2xpa_popul |   .0345592   .0088134     3.92   0.000     .0172853    .0518331
        xage |  -.0076471   .0015101    -5.06   0.000    -.0106069   -.0046873
      female |  -.0207302   .0037477    -5.53   0.000    -.0280755   -.0133848
    educ_med |   -.009807   .0052019    -1.89   0.059    -.0200026    .0003886
   educ_high |  -.0126946   .0055673    -2.28   0.023    -.0236062   -.0017829
emp_fulltime |   .0049963   .0049641     1.01   0.314    -.0047331    .0147257
 emp_retired |   -.003156   .0067084    -0.47   0.638    -.0163041    .0099922
   emp_unemp |  -.0039527    .008356    -0.47   0.636    -.0203301    .0124247
  income_med |   -.007766   .0046131    -1.68   0.092    -.0168075    .0012756
  incom_high |  -.0179054   .0049277    -3.63   0.000    -.0275635   -.0082472
       _cons |   .1203521    .013697     8.79   0.000     .0935065    .1471977
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0025254    .000712      .0014532    .0043886
-----------------------------+------------------------------------------------
               var(Residual) |   .0617883   .0006472      .0605328    .0630699
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 553.18        Prob >= chibar2 = 0.0000

.    mixed anyviolence personalist v2paviol $dvar if lose==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -540.27782  
Iteration 1:  Log likelihood = -540.27782  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(11)    = 137.39
Log likelihood = -540.27782                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0184704   .0066842     2.76   0.006     .0053696    .0315712
    v2paviol |  -.0083336   .0032505    -2.56   0.010    -.0147044   -.0019628
        xage |  -.0077408   .0015101    -5.13   0.000    -.0107006   -.0047809
      female |  -.0210173   .0037471    -5.61   0.000    -.0283615   -.0136731
    educ_med |  -.0102951    .005204    -1.98   0.048    -.0204946   -.0000955
   educ_high |  -.0145096   .0055474    -2.62   0.009    -.0253824   -.0036368
emp_fulltime |   .0054017    .004965     1.09   0.277    -.0043295     .015133
 emp_retired |  -.0030433   .0067117    -0.45   0.650     -.016198    .0101114
   emp_unemp |   -.003478   .0083584    -0.42   0.677    -.0198602    .0129042
  income_med |  -.0078072   .0046146    -1.69   0.091    -.0168518    .0012373
  incom_high |  -.0185035   .0049247    -3.76   0.000    -.0281558   -.0088513
       _cons |   .1419596   .0143337     9.90   0.000     .1138661    .1700531
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0029535   .0008354      .0016966    .0051416
-----------------------------+------------------------------------------------
               var(Residual) |   .0618041   .0006474      .0605482    .0630861
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 618.18        Prob >= chibar2 = 0.0000

. 
. * Interaction term among winners and losers *
.   mixed anyviolence C.personalist##C.jan1pers loser $dvar ||country: 

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -155.36114  
Iteration 1:  Log likelihood = -155.36114  

Computing standard errors ...

Mixed-effects ML regression                         Number of obs    =  28,007
Group variable: country                             Number of groups =      28
                                                    Obs per group:
                                                                 min =     244
                                                                 avg = 1,000.2
                                                                 max =   2,879
                                                    Wald chi2(13)    =  184.37
Log likelihood = -155.36114                         Prob > chi2      =  0.0000

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |  -.0124061   .0116155    -1.07   0.285    -.0351721    .0103599
  jan1persparty |   .0356902   .0373002     0.96   0.339    -.0374169    .1087974
                |
  c.personalist#|
c.jan1persparty |    .027303   .0187451     1.46   0.145    -.0094366    .0640427
                |
          loser |   .0098164   .0034756     2.82   0.005     .0030043    .0166284
           xage |  -.0073859   .0011947    -6.18   0.000    -.0097275   -.0050444
         female |  -.0178218   .0029567    -6.03   0.000    -.0236169   -.0120268
       educ_med |  -.0106067   .0040517    -2.62   0.009    -.0185479   -.0026655
      educ_high |  -.0144257   .0043879    -3.29   0.001    -.0230258   -.0058257
   emp_fulltime |   .0018851   .0039848     0.47   0.636     -.005925    .0096951
    emp_retired |  -.0096512   .0052687    -1.83   0.067    -.0199777    .0006753
      emp_unemp |    -.00576   .0065743    -0.88   0.381    -.0186454    .0071253
     income_med |  -.0090001   .0036438    -2.47   0.014    -.0161419   -.0018584
     incom_high |  -.0195125   .0038973    -5.01   0.000     -.027151    -.011874
          _cons |   .1124332   .0211291     5.32   0.000      .071021    .1538454
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0020531   .0005698      .0011918     .003537
-----------------------------+------------------------------------------------
               var(Residual) |   .0589974   .0004988      .0580278    .0599832
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 728.79        Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [anyviolence]personalist + .2*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0069455   .0082774    -0.84   0.401     -.023169    .0092779
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [anyviolence]personalist + .7*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .006706   .0050484     1.33   0.184    -.0031886    .0166006
------------------------------------------------------------------------------

. * Interaction term among losers *
.   mixed anyviolence C.personalist##C.jan1pers $dvar if loser==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -542.45217  
Iteration 1:  Log likelihood = -542.45217  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(12)    = 132.76
Log likelihood = -542.45217                          Prob > chi2      = 0.0000

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0031717   .0179159     0.18   0.859    -.0319428    .0382862
  jan1persparty |   .0269828   .0421062     0.64   0.522    -.0555439    .1095095
                |
  c.personalist#|
c.jan1persparty |   .0354861   .0281451     1.26   0.207    -.0196772    .0906495
                |
           xage |   -.007839   .0015098    -5.19   0.000    -.0107981   -.0048799
         female |  -.0212782   .0037468    -5.68   0.000    -.0286218   -.0139346
       educ_med |  -.0102966   .0052055    -1.98   0.048    -.0204992   -.0000939
      educ_high |  -.0148123   .0055504    -2.67   0.008    -.0256908   -.0039338
   emp_fulltime |   .0053203   .0049655     1.07   0.284    -.0044119    .0150526
    emp_retired |  -.0035874   .0067107    -0.53   0.593    -.0167402    .0095654
      emp_unemp |  -.0036048   .0083594    -0.43   0.666    -.0199889    .0127792
     income_med |  -.0081196   .0046143    -1.76   0.078    -.0171634    .0009243
     incom_high |  -.0190228   .0049226    -3.86   0.000    -.0286709   -.0093747
          _cons |   .1201966   .0240065     5.01   0.000     .0731447    .1672485
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026168    .000738      .0015056    .0045481
-----------------------------+------------------------------------------------
               var(Residual) |   .0618298   .0006476      .0605734    .0631123
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 542.79        Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [anyviolence]personalist + .2*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .010269   .0127853     0.80   0.422    -.0147897    .0353276
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [anyviolence]personalist + .7*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |    .028012     .00685     4.09   0.000     .0145863    .0414377
------------------------------------------------------------------------------

.   
.                    ** Lack of within variation for personalist party voters for s
> ome countries **
.                   xtsum personalist if cowcode==385 |cowcode==390 |cowcode==380 |
> cowcode==375 |cowcode==344 ///
>                         |cowcode==339 |cowcode==255 |cowcode==235 |cowcode== 200,
> i(cowcode)
warning: existing panel variable is not cowcode

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
person~t overall |  .0810642   .2729459          0          1 |     N =   11238
         between |             .3333333          0          1 |     n =       9
         within  |                    0   .0810642   .0810642 | T-bar = 1248.67

.                   gen novar = cowcode==385 |cowcode==390 |cowcode==380 |cowcode==
> 375 |cowcode==344 ///
>                         |cowcode==339 |cowcode==255 |cowcode==235 |cowcode== 200

.                   tab novar

      novar |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     17,061       60.10       60.10
          1 |     11,329       39.90      100.00
------------+-----------------------------------
      Total |     28,390      100.00

. 
.                   qui reghdfe anyviolence personalist jan1persparty leftid righti
> d $dvar if loser==1 & novar==0,a(country)  

.                   table country_name if e(sample)==1,stat(sd personalist)

---------------------------------------
                  |  Standard deviation
------------------+--------------------
Country name      |                    
  Austria         |                   0
  Bulgaria        |             .435303
  Czech Republic  |            .3167904
  Estonia         |            .4660668
  France          |            .4016035
  Georgia         |            .3727545
  Hungary         |            .3589793
  Iceland         |            .4416267
  Italy           |            .4986375
  Lithuania       |            .3847922
  Netherlands     |            .3298858
  North Macedonia |            .3154049
  Poland          |                   0
  Romania         |            .3884045
  Serbia          |            .5014941
  Slovakia        |            .4860549
  Slovenia        |            .4935342
  Spain           |            .3810844
  Total           |            .4370226
---------------------------------------

.                   qui replace novar=1 if country_name=="Poland" | country_name=="
> Austria"

.                   
.                   * Check that average effect of personalist among loses is the s
> ame when dropping countries with no personalist within variation *
.                   mixed anyviolence personalist $dvar if lose==1   ||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -543.51411  
Iteration 1:  Log likelihood = -543.51411  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(10)    = 130.65
Log likelihood = -543.51411                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0247382   .0061949     3.99   0.000     .0125963      .03688
        xage |  -.0078531   .0015098    -5.20   0.000    -.0108123   -.0048939
      female |  -.0212617   .0037467    -5.67   0.000    -.0286051   -.0139183
    educ_med |  -.0100912   .0052039    -1.94   0.052    -.0202907    .0001083
   educ_high |  -.0145929   .0055483    -2.63   0.009    -.0254673   -.0037185
emp_fulltime |   .0052664   .0049656     1.06   0.289     -.004466    .0149988
 emp_retired |  -.0035195   .0067105    -0.52   0.600    -.0166717    .0096328
   emp_unemp |  -.0034726   .0083592    -0.42   0.678    -.0198564    .0129112
  income_med |  -.0080621   .0046144    -1.75   0.081    -.0171062     .000982
  incom_high |  -.0189411   .0049227    -3.85   0.000    -.0285894   -.0092929
       _cons |   .1328686   .0135533     9.80   0.000     .1063046    .1594326
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0027015   .0007588      .0015578    .0046848
-----------------------------+------------------------------------------------
               var(Residual) |   .0618341   .0006477      .0605776    .0631167
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 618.13        Prob >= chibar2 = 0.0000

.                   mixed anyviolence personalist $dvar if lose==1 & novar==0 ||cou
> ntry:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -1244.5389  
Iteration 1:  Log likelihood = -1244.5389  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  9,447
Group variable: country                              Number of groups =     16
                                                     Obs per group:
                                                                  min =    164
                                                                  avg =  590.4
                                                                  max =  1,337
                                                     Wald chi2(10)    = 105.74
Log likelihood = -1244.5389                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0228493   .0069582     3.28   0.001     .0092115     .036487
        xage |  -.0081231   .0023302    -3.49   0.000    -.0126902   -.0035559
      female |  -.0301579   .0057883    -5.21   0.000    -.0415027   -.0188131
    educ_med |  -.0138495   .0078626    -1.76   0.078    -.0292599     .001561
   educ_high |  -.0249571   .0086118    -2.90   0.004    -.0418358   -.0080783
emp_fulltime |   .0086837   .0076543     1.13   0.257    -.0063184    .0236857
 emp_retired |  -.0146209   .0102696    -1.42   0.155    -.0347489    .0055071
   emp_unemp |  -.0149725   .0128092    -1.17   0.242     -.040078    .0101331
  income_med |  -.0052996   .0069339    -0.76   0.445    -.0188899    .0082907
  incom_high |  -.0280409   .0074888    -3.74   0.000    -.0427188   -.0133631
       _cons |   .1606994   .0204707     7.85   0.000     .1205775    .2008213
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0033327   .0012442      .0016033    .0069276
-----------------------------+------------------------------------------------
               var(Residual) |   .0757891   .0011037      .0736565    .0779835
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 302.67        Prob >= chibar2 = 0.0000

. 
. * Reported interaction with kernel estimator * FE so drop countries with no withi
> n variation in personalist voter
.   reghdfe anyviolence C.personalist##C.jan1persparty   $dvar if loser==1,a(countr
> y)
note: jan1persparty is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: jan1persparty omitted because of collinearity

HDFE Linear regression                            Number of obs   =     18,257
Absorbing 1 HDFE group                            F(  11,  18218) =      12.12
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0469
                                                  Adj R-squared   =     0.0449
                                                  Within R-sq.    =     0.0073
                                                  Root MSE        =     0.2487

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0066994   .0181092     0.37   0.711    -.0287962    .0421951
  jan1persparty |          0  (omitted)
                |
  c.personalist#|
c.jan1persparty |   .0328741   .0283354     1.16   0.246     -.022666    .0884142
                |
           xage |  -.0078468    .001511    -5.19   0.000    -.0108085   -.0048851
         female |   -.021279   .0037485    -5.68   0.000    -.0286265   -.0139316
       educ_med |  -.0102071   .0052149    -1.96   0.050    -.0204289    .0000146
      educ_high |   -.014536   .0055578    -2.62   0.009    -.0254299   -.0036422
   emp_fulltime |   .0050757   .0049704     1.02   0.307    -.0046668    .0148182
    emp_retired |  -.0035454   .0067159    -0.53   0.598    -.0167091    .0096183
      emp_unemp |  -.0028619    .008376    -0.34   0.733    -.0192797     .013556
     income_med |  -.0079411   .0046203    -1.72   0.086    -.0169974    .0011152
     incom_high |  -.0187821   .0049303    -3.81   0.000    -.0284459   -.0091182
          _cons |   .1330361     .00929    14.32   0.000     .1148268    .1512455
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        28           0          28     |
-----------------------------------------------------+

.     lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  personalist + .2*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0132742   .0129445     1.03   0.305    -.0120981    .0386466
------------------------------------------------------------------------------

.         lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  personalist + .7*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0297113   .0069004     4.31   0.000     .0161858    .0432368
------------------------------------------------------------------------------

.   reghdfe anyviolence C.personalist##C.jan1persparty   $dvar if loser==1 & novar=
> =0,a(country)
note: jan1persparty is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: jan1persparty omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,447
Absorbing 1 HDFE group                            F(  11,   9420) =       9.79
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0498
                                                  Adj R-squared   =     0.0472
                                                  Within R-sq.    =     0.0113
                                                  Root MSE        =     0.2754

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |    .003735    .020097     0.19   0.853    -.0356594    .0431294
  jan1persparty |          0  (omitted)
                |
  c.personalist#|
c.jan1persparty |    .034514   .0313979     1.10   0.272    -.0270327    .0960608
                |
           xage |  -.0080603    .002333    -3.45   0.001    -.0126336    -.003487
         female |  -.0301879   .0057927    -5.21   0.000    -.0415428   -.0188329
       educ_med |  -.0139017   .0078827    -1.76   0.078    -.0293534      .00155
      educ_high |  -.0246106   .0086323    -2.85   0.004    -.0415319   -.0076893
   emp_fulltime |   .0084629   .0076633     1.10   0.269    -.0065588    .0234845
    emp_retired |  -.0148412   .0102811    -1.44   0.149    -.0349943     .005312
      emp_unemp |  -.0145292   .0128422    -1.13   0.258    -.0397028    .0106443
     income_med |  -.0052242   .0069392    -0.75   0.452    -.0188264    .0083781
     incom_high |  -.0280511   .0074959    -3.74   0.000    -.0427447   -.0133575
          _cons |   .1641913   .0144627    11.35   0.000     .1358413    .1925414
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        16           0          16     |
-----------------------------------------------------+

.     lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  personalist + .2*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0106378   .0143777     0.74   0.459    -.0175457    .0388213
------------------------------------------------------------------------------

.         lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  personalist + .7*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0278948   .0076704     3.64   0.000     .0128592    .0429305
------------------------------------------------------------------------------

.   interflex anyviolence personalist jan1persparty $dvar if loser==1,fe(panel) typ
> e(kernel)bw(.25)
Fixed effects included; clustered standard errors highly recommended

.         mat list r(margeff) 

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1           0   .03790986   .02372237  -.00858513   .08440484
 r2   .02040816   .03518424   .02193974  -.00781686   .07818535
 r3   .04081633   .03278814   .02031652  -.00703152   .07260779
 r4   .06122449   .03069297   .01884226  -.00623719   .06762312
 r5   .08163265   .02887098   .01750652  -.00544117   .06318313
 r6   .10204082   .02729562   .01629895  -.00464974   .05924099
 r7   .12244898    .0259417   .01520934  -.00386806   .05575146
 r8   .14285714   .02478561   .01422765  -.00310007    .0526713
 r9   .16326531   .02380541   .01334413  -.00234859   .04995942
r10   .18367347   .02298088   .01254934  -.00161538   .04757714
r11   .20408163   .02229355   .01183429  -.00090124   .04548834
r12    .2244898   .02172669   .01119045  -.00020619   .04365958
r13   .24489796   .02126528   .01060984   .00047038   .04206018
r14   .26530612   .02089591   .01008507   .00112953   .04066229
r15   .28571429   .02060678   .00960942   .00177266    .0394409
r16   .30612245   .02038759    .0091768   .00240139   .03837379
r17   .32653061   .02022952   .00878181   .00301748   .03744156
r18   .34693878   .02012517   .00841973   .00362281   .03662754
r19   .36734694   .02006854   .00808647   .00421936   .03591773
r20    .3877551   .02005504    .0077786   .00480925   .03530082
r21   .40816327   .02008148   .00749333   .00539482   .03476813
r22   .42857143   .02014614   .00722843   .00597867   .03431361
r23   .44897959   .02024883   .00698231   .00656375    .0339339
r24   .46938776   .02039092   .00675392   .00715348   .03362837
r25   .48979592   .02057548   .00654282   .00775179   .03339917
r26   .51020408   .02080724   .00634912    .0083632   .03325128
r27   .53061224    .0210927   .00617351   .00899285   .03319255
r28   .55102041   .02144004   .00601721   .00964652   .03323355
r29   .57142857   .02185901   .00588196   .01033059   .03338743
r30   .59183673   .02236073   .00576988   .01105197   .03366949
r31    .6122449   .02295732    .0056834   .01181807   .03409657
r32   .63265306   .02366145   .00562501   .01263664   .03468626
r33   .65306122   .02448574   .00559707   .01351568   .03545579
r34   .67346939   .02544205   .00560155   .01446321   .03642089
r35   .69387755   .02654077   .00563976   .01548704   .03759451
r36   .71428571   .02779007   .00571219   .01659437   .03898576
r37   .73469388   .02919518   .00581841   .01779132   .04059905
r38   .75510204   .03075794   .00595705   .01908234   .04243354
r39    .7755102   .03247638   .00612598   .02046967   .04448309
r40   .79591837   .03434466   .00632249   .02195282   .04673651
r41   .81632653   .03635318   .00654348    .0235282   .04917817
r42   .83673469   .03848892   .00678577   .02518905   .05178878
r43   .85714286   .04073591   .00704627   .02692548   .05454634
r44   .87755102   .04307593   .00732214   .02872479   .05742706
r45   .89795918   .04548909   .00761095    .0305719   .06040628
r46   .91836735   .04795452    .0079107   .03244984   .06345919
r47   .93877551   .05045094   .00821986   .03434031   .06656156
r48   .95918367   .05295714   .00853741   .03622413   .06969015
r49   .97959184   .05545236   .00886276   .03808166   .07282306
r50           1   .05791653   .00919578   .03989313   .07593993

.   interflex anyviolence personalist jan1persparty $dvar if loser==1 & novar==0,fe
> (panel) type(kernel)bw(.25)
Fixed effects included; clustered standard errors highly recommended

.         mat list r(margeff)     /* Losing personalist party voter moves from 0% t
> o 4% more likely to justify violence than nonpersonalist losing voter */ 

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806   .01710974   .01069663  -.00385527   .03807475
 r2   .22771729   .01693839    .0103669  -.00338036   .03725713
 r3   .24380651    .0168172   .01006454  -.00290893   .03654333
 r4   .25989573   .01674046    .0097871  -.00244191   .03592284
 r5   .27598496   .01670307   .00953225  -.00197981   .03538594
 r6   .29207418   .01670044   .00929776  -.00152284   .03492372
 r7    .3081634   .01672857   .00908154  -.00107091   .03452805
 r8   .32425263   .01678395   .00888162  -.00062372   .03419161
 r9   .34034185   .01686358   .00869623  -.00018072   .03390788
r10   .35643107   .01696498   .00852373   .00025879   .03367118
r11    .3725203   .01708616   .00836265   .00069567   .03347665
r12   .38860952   .01722564   .00821172   .00113096   .03332031
r13   .40469874   .01738243   .00806984   .00156582   .03319903
r14   .42078797   .01755609   .00793613   .00200156   .03311062
r15   .43687719   .01774673   .00780989   .00243962   .03305384
r16   .45296641   .01795502   .00769066    .0028816   .03302844
r17   .46905564   .01818225   .00757821   .00332923   .03303527
r18   .48514486   .01843031   .00747255   .00378437   .03307624
r19   .50123408   .01870174   .00737396   .00424904   .03315445
r20    .5173233   .01899977   .00728299   .00472537   .03327417
r21   .53341253   .01932824   .00720046   .00521561   .03344088
r22   .54950175   .01969167   .00712747   .00572209   .03366125
r23   .56559097   .02009515   .00706539   .00624725   .03394305
r24    .5816802   .02054433   .00701582   .00679357   .03429508
r25   .59776942   .02104527   .00698058   .00736359   .03472694
r26   .61385864   .02160436   .00696159    .0079599   .03524883
r27   .62994787   .02222813   .00696086   .00858511   .03587116
r28   .64603709   .02292306   .00698032   .00924188   .03660424
r29   .66212631   .02369537   .00702178   .00993293   .03745782
r30   .67821554    .0245508   .00708677   .01066098   .03844061
r31   .69430476   .02549433   .00717645   .01142875    .0395599
r32   .71039398      .02653   .00729153   .01223887   .04082114
r33   .72648321   .02766071   .00743225   .01309377   .04222765
r34   .74257243   .02888798   .00759833   .01399553   .04378042
r35   .75866165   .03021189   .00778901   .01494571   .04547807
r36   .77475088   .03163099   .00800313   .01594514   .04731683
r37    .7908401   .03314228   .00823917    .0169938   .04929076
r38   .80692932   .03474125   .00849539    .0180906   .05139191
r39   .82301855   .03642203   .00876989   .01923336    .0536107
r40   .83910777   .03817744   .00906074   .02041872   .05593617
r41   .85519699   .03999929   .00936605   .02164217   .05835641
r42   .87128621   .04187847   .00968403   .02289812   .06085881
r43   .88737544   .04380523   .01001306      .02418   .06343046
r44   .90346466   .04576938   .01035172   .02548038   .06605838
r45   .91955388   .04776044   .01069883   .02679111   .06872977
r46   .93564311   .04976787   .01105345   .02810351   .07143223
r47   .95173233   .05178117   .01141486   .02940846   .07415389
r48   .96782155   .05379003   .01178262   .03069652   .07688353
r49   .98391078   .05578437   .01215648   .03195812   .07961063
r50           1   .05775448   .01253643   .03318353   .08232544

.   mat r = r(margeff)

.   gen x=.
(28,390 missing values generated)

.   gen b=.
(28,390 missing values generated)

.   gen hi=.
(28,390 missing values generated)

.   gen lo=.
(28,390 missing values generated)

.   gen n=_n

.   forval i=1/50 {
  2.         qui replace x  = r[`i',1] if n==`i'
  3.         qui replace b  = r[`i',2] if n==`i'
  4.         qui replace hi  = r[`i',5] if n==`i'
  5.         qui replace lo  = r[`i',4] if n==`i'
  6.   }

.   
. twoway (rarea hi lo x,col(gs14)) (line b x,lcol(gs1)lpat(solid)yline(0,lpat(solid
> ))ylab(0(.02).08) ///
>         xtit(Ruling party personalism)legend(off)ytit("Marginal effect of a {bf:p
> ersonalist party voter}",size(small)) ///
>         tit("Personalist voter effect" ) ///
>         note(Sample of voters who backed a losing political party in election, //
> /
>         size(small)pos(6))saving(h1.gph,replace)) 
file h1.gph saved

.         gr export "$dir\golden\T-EVS-Justify-Violence-PP1.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -EVS-Justify-Violence-PP1.pdf saved as PDF format

.         
.          * Check when measuring RULING party personalism with V-Parties data *
.         reghdfe anyviolence C.personalist##C.ruling   $dvar if loser==1 & novar==
> 0,a(country) 
note: rulingv2paind is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: rulingv2paind omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,023
Absorbing 1 HDFE group                            F(  11,   8998) =       9.83
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0483
                                                  Adj R-squared   =     0.0458
                                                  Within R-sq.    =     0.0119
                                                  Root MSE        =     0.2800

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0148435   .0084611     1.75   0.079    -.0017422    .0314293
  rulingv2paind |          0  (omitted)
                |
  c.personalist#|
c.rulingv2paind |    .016962   .0071591     2.37   0.018     .0029285    .0309955
                |
           xage |  -.0081068   .0024305    -3.34   0.001    -.0128712   -.0033424
         female |  -.0307608   .0060318    -5.10   0.000    -.0425845   -.0189371
       educ_med |  -.0143995   .0081647    -1.76   0.078    -.0304042    .0016052
      educ_high |  -.0254994   .0089446    -2.85   0.004    -.0430328    -.007966
   emp_fulltime |   .0078206   .0079843     0.98   0.327    -.0078304    .0234716
    emp_retired |  -.0155914   .0106486    -1.46   0.143    -.0364651    .0052822
      emp_unemp |  -.0119811   .0137534    -0.87   0.384    -.0389409    .0149788
     income_med |   -.004957   .0072131    -0.69   0.492    -.0190962    .0091823
     incom_high |  -.0280208   .0078033    -3.59   0.000    -.0433171   -.0127245
          _cons |   .1695358    .015073    11.25   0.000     .1399893    .1990822
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        14           0          14     |
-----------------------------------------------------+

.                 lincom personalist + c.personalist#c.ruling *(.22 - 1.33)

 ( 1)  personalist - 1.11*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0039843   .0142599    -0.28   0.780    -.0319369    .0239683
------------------------------------------------------------------------------

.                 lincom personalist + c.personalist#c.ruling *(.22 + 1.33)

 ( 1)  personalist + 1.55*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0411346   .0099462     4.14   0.000     .0216377    .0606315
------------------------------------------------------------------------------

.         reghdfe anyviolence C.personalist##C.ruling $dvar if loser==1,a(country)
note: rulingv2paind is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: rulingv2paind omitted because of collinearity

HDFE Linear regression                            Number of obs   =     17,213
Absorbing 1 HDFE group                            F(  11,  17178) =      12.08
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0463
                                                  Adj R-squared   =     0.0444
                                                  Within R-sq.    =     0.0077
                                                  Root MSE        =     0.2532

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0170259   .0076072     2.24   0.025      .002115    .0319368
  rulingv2paind |          0  (omitted)
                |
  c.personalist#|
c.rulingv2paind |   .0168528    .006471     2.60   0.009      .004169    .0295367
                |
           xage |  -.0079792   .0015823    -5.04   0.000    -.0110806   -.0048778
         female |  -.0214206   .0039349    -5.44   0.000    -.0291334   -.0137079
       educ_med |  -.0102086   .0054401    -1.88   0.061    -.0208716    .0004545
      educ_high |  -.0155317   .0057886    -2.68   0.007    -.0268781   -.0041854
   emp_fulltime |   .0047376   .0051963     0.91   0.362    -.0054477    .0149228
    emp_retired |  -.0039898   .0070047    -0.57   0.569    -.0177197      .00974
      emp_unemp |  -.0021905   .0090732    -0.24   0.809    -.0199748    .0155938
     income_med |  -.0073133    .004844    -1.51   0.131    -.0168081    .0021815
     incom_high |   -.018531   .0051752    -3.58   0.000    -.0286748   -.0083872
          _cons |   .1352256   .0097199    13.91   0.000     .1161735    .1542776
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        24           0          24     |
-----------------------------------------------------+

.                 lincom personalist + c.personalist#c.ruling *(.22 - 1.33)

 ( 1)  personalist - 1.11*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0016808   .0128559    -0.13   0.896    -.0268797    .0235182
------------------------------------------------------------------------------

.                 lincom personalist + c.personalist#c.ruling *(.22 + 1.33)

 ( 1)  personalist + 1.55*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0431478   .0089742     4.81   0.000     .0255575     .060738
------------------------------------------------------------------------------

.         reghdfe anyviolence C.personalist##C.ruling $dvar if loser==1,a(country)c
> luster(v2paid)
note: rulingv2paind is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: rulingv2paind omitted because of collinearity

HDFE Linear regression                            Number of obs   =     17,213
Absorbing 1 HDFE group                            F(  11,    133) =       7.78
Statistics robust to heteroskedasticity           Prob > F        =     0.0000
                                                  R-squared       =     0.0463
                                                  Adj R-squared   =     0.0444
                                                  Within R-sq.    =     0.0077
Number of clusters (v2paid)  =        134         Root MSE        =     0.2532

                                  (Std. err. adjusted for 134 clusters in v2paid)
---------------------------------------------------------------------------------
                |               Robust
    anyviolence | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0170259   .0107989     1.58   0.117    -.0043339    .0383856
  rulingv2paind |          0  (omitted)
                |
  c.personalist#|
c.rulingv2paind |   .0168528   .0097161     1.73   0.085    -.0023652    .0360709
                |
           xage |  -.0079792   .0016595    -4.81   0.000    -.0112616   -.0046967
         female |  -.0214206   .0045075    -4.75   0.000    -.0303363    -.012505
       educ_med |  -.0102086   .0051154    -2.00   0.048    -.0203267   -.0000904
      educ_high |  -.0155317    .006838    -2.27   0.025    -.0290571   -.0020064
   emp_fulltime |   .0047376   .0057257     0.83   0.409    -.0065876    .0160627
    emp_retired |  -.0039898   .0076672    -0.52   0.604    -.0191552    .0111755
      emp_unemp |  -.0021905   .0089225    -0.25   0.806    -.0198389     .015458
     income_med |  -.0073133   .0040251    -1.82   0.071    -.0152748    .0006482
     incom_high |   -.018531   .0044717    -4.14   0.000    -.0273758   -.0096862
          _cons |   .1352256   .0099932    13.53   0.000     .1154595    .1549917
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        24           0          24     |
-----------------------------------------------------+

.                 lincom personalist + c.personalist#c.ruling *(.22 - 1.33)

 ( 1)  personalist - 1.11*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0016808   .0178161    -0.09   0.925    -.0369203    .0335588
------------------------------------------------------------------------------

.                 lincom personalist + c.personalist#c.ruling *(.22 + 1.33)

 ( 1)  personalist + 1.55*c.personalist#c.rulingv2paind = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0431478   .0150148     2.87   0.005     .0134491    .0728465
------------------------------------------------------------------------------

.                 
.         * Adjust for types of populist party *
.         interflex polviolence personalist jan1persparty antielite people rightid 
> leftid $dvar ///
>                 if loser==1 & novar==0,fe(country)type(kernel)bw(.25)
Fixed effects included; clustered standard errors highly recommended

.         mat list r(margeff)

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.10304611   .08404099  -.26776342   .06167119
 r2   .22771729  -.09365504   .08074287  -.25190816   .06459807
 r3   .24380651  -.08455495   .07765992   -.2367656    .0676557
 r4   .25989573  -.07576112   .07477729  -.22232191   .07079967
 r5   .27598496  -.06728702   .07208088  -.20856296   .07398891
 r6   .29207418  -.05914416    .0695574  -.19547417   .07718585
 r7    .3081634    -.051342   .06719434  -.18304048   .08035648
 r8   .32425263  -.04388779   .06497997   -.1712462   .08347061
 r9   .34034185  -.03678643   .06290343  -.16007488   .08650202
r10   .35643107  -.03004028   .06095467  -.14950924   .08942869
r11    .3725203  -.02364903   .05912456  -.13953103   .09223298
r12   .38860952  -.01760955   .05740485  -.13012098   .09490189
r13   .40469874  -.01191572    .0557883  -.12125877   .09742733
r14   .42078797   -.0065583   .05426868  -.11292297   .09980637
r15   .43687719  -.00152478   .05284091  -.10509105   .10204149
r16   .45296641    .0032007   .05150105   -.0977395   .10414091
r17   .46905564   .00763752   .05024648  -.09084377   .10611881
r18   .48514486   .01180859   .04907594  -.08437848   .10799566
r19   .50123408   .01574041   .04798961   -.0783175   .10979832
r20    .5173233   .01946309   .04698922  -.07263409   .11156027
r21   .53341253   .02301022   .04607805  -.06730109   .11332153
r22   .54950175   .02641876   .04526094  -.06229106   .11512857
r23   .56559097   .02972875   .04454425  -.05757638   .11703388
r24    .5816802   .03298294    .0439357  -.05312945   .11909532
r25   .59776942   .03622628   .04344411  -.04892261   .12137518
r26   .61385864    .0395053   .04307912  -.04492823   .12393883
r27   .62994787   .04286725   .04285069  -.04111855   .12685306
r28   .64603709   .04635923   .04276855   -.0374656   .13018405
r29   .66212631   .05002713   .04284168  -.03394103   .13399529
r30   .67821554   .05391456   .04307764  -.03051606   .13834518
r31   .69430476   .05806165   .04348204  -.02716158   .14328488
r32   .71039398   .06250399   .04405814  -.02384838   .14885636
r33   .72648321   .06727159   .04480658   -.0205477   .15509088
r34   .74257243   .07238797   .04572532  -.01723201   .16200795
r35   .75866165   .07786951   .04680978  -.01387597   .16961499
r36   .77475088   .08372493   .04805315  -.01045751   .17790738
r37    .7908401    .0899552   .04944685  -.00695885   .18686926
r38   .80692932   .09655349   .05098103  -.00336748   .19647447
r39   .82301855   .10350559   .05264505   .00032318     .206688
r40   .83910777   .11079042   .05442807   .00411336   .21746747
r41   .85519699   .11838076   .05631941   .00799675   .22876477
r42   .87128621   .12624413   .05830893   .01196073   .24052754
r43   .88737544   .13434374   .06038736   .01598669   .25270078
r44   .90346466   .14263935   .06254642   .02005062   .26522808
r45   .91955388    .1510883   .06477902   .02412376   .27805284
r46   .93564311   .15964627   .06707926   .02817334    .2911192
r47   .95173233   .16826811    .0694425   .03216332    .3043729
r48   .96782155   .17690834   .07186528   .03605497   .31776171
r49   .98391078   .18552178   .07434534    .0398076   .33123596
r50           1   .19406385   .07688143   .04337901   .34474869

. 
.   * Split data by personalist voter *
.           interflex anyviolence loser jan1persparty   $dvar if personalist==0,fe(
> country) type(kernel)bw(.3)
Fixed effects included; clustered standard errors highly recommended

.           mat r=r(margeff)

.           gen b1=.
(28,390 missing values generated)

.           gen hi1=.
(28,390 missing values generated)

.           gen lo1=.
(28,390 missing values generated)

.            forval i=1/50 {
  2.                 qui replace x  = r[`i',1] if n==`i'
  3.                 qui replace b1  = r[`i',2] if n==`i'
  4.                 qui replace hi1  = r[`i',5] if n==`i'
  5.                 qui replace lo1  = r[`i',4] if n==`i'
  6.           }

.           interflex anyviolence loser jan1persparty   $dvar if personalist==1,fe(
> country) type(kernel)bw(.3)
Fixed effects included; clustered standard errors highly recommended

.           mat r=r(margeff)

.           gen b2=.
(28,390 missing values generated)

.           gen hi2=.
(28,390 missing values generated)

.           gen lo2=.
(28,390 missing values generated)

.            forval i=1/50 {
  2.                 qui replace x  = r[`i',1] if n==`i'
  3.                 qui replace b2  = r[`i',2] if n==`i'
  4.                 qui replace hi2  = r[`i',5] if n==`i'
  5.                 qui replace lo2  = r[`i',4] if n==`i'
  6.           }

. 
.         twoway (rarea hi1 lo1 x,col(gs14)) (rarea hi2 lo2 x,col(gs14))  (line b1 
> x,lcol(gs1)lpat(dash))  ///
>                 (line b2 x,lcol(gs1)lpat(solid)yline(0,lpat(solid)lcol(gs10))ylab
> (-.05(.05).15) ///
>                 xtit(Ruling party personalism)legend(off)ytit("Marginal effect of
>  a {bf:voting for losing party}",size(small)) ///
>                 tit(Loser effect) note("Sample of all voters: winners & losers",s
> ize(small)pos(6)) ///
>                 text(0.09 0.7 "Personalist" "voters  ",place(e)size(.3cm)) ///
>                 text(-0.05 0.5 "Non-personalist" "voters  ",place(e)size(.3cm))sa
> ving(h2.gph,replace))
file h2.gph saved

.         gr export "$dir\golden\T-EVS-Justify-Violence-PP2.pdf",as(pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\T
    > -EVS-Justify-Violence-PP2.pdf saved as PDF format

.         gr combine h1.gph h2.gph,col(2)xsize(8)tit("Personalist party voters more
>  likely to justify political violence when losing ruling party is more personalis
> t")

.         gr export "$dir\golden\Ch6-EVS-Justify-Violence-Party-Personalism.pdf",as
> (pdf)replace 
file
    C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\golden\C
    > h6-EVS-Justify-Violence-Party-Personalism.pdf saved as PDF format

.                  
.   
. 
. * Checks by split samples: high/low ruling party personalism and loser *
.   centile jan1persparty  if personalist~=. & anyviolence~=.,centile(50)

                                                          Binom. interp.   
    Variable |       Obs  Percentile    Centile        [95% conf. interval]
-------------+-------------------------------------------------------------
jan1perspa~y |    28,124         50    .3265174        .3265174    .4580351

.   global cut = r(c_1) + .0001

.   mixed anyviolence personalist v2xpa_popul $dvar if loser==0 & jan1persparty<$cu
> t ||country: 

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood =     608.24  
Iteration 1:  Log likelihood =     608.24  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  4,485
Group variable: country                              Number of groups =     11
                                                     Obs per group:
                                                                  min =    174
                                                                  avg =  407.7
                                                                  max =    892
                                                     Wald chi2(11)    =  24.33
Log likelihood =     608.24                          Prob > chi2      = 0.0114

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |  -.0295662   .0303095    -0.98   0.329    -.0889716    .0298393
 v2xpa_popul |  -.0687857    .116952    -0.59   0.556    -.2980075     .160436
        xage |  -.0067781   .0026406    -2.57   0.010    -.0119536   -.0016026
      female |   -.008967   .0064077    -1.40   0.162    -.0215259    .0035919
    educ_med |   -.014239   .0088276    -1.61   0.107    -.0315407    .0030627
   educ_high |  -.0066272   .0090997    -0.73   0.466    -.0244624    .0112079
emp_fulltime |  -.0154995   .0087126    -1.78   0.075    -.0325759    .0015768
 emp_retired |  -.0131288   .0109871    -1.19   0.232    -.0346631    .0084055
   emp_unemp |  -.0030478   .0208778    -0.15   0.884    -.0439675    .0378718
  income_med |  -.0107371    .008259    -1.30   0.194    -.0269244    .0054502
  incom_high |  -.0139261   .0086908    -1.60   0.109    -.0309597    .0031075
       _cons |   .1401233   .0309732     4.52   0.000     .0794169    .2008296
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0012595   .0006066      .0004901    .0032369
-----------------------------+------------------------------------------------
               var(Residual) |   .0443739   .0009383      .0425726    .0462515
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 71.38         Prob >= chibar2 = 0.0000

.                 xtsum personalist if e(sample),i(cowcode)

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
person~t overall |  .1723523   .3777285          0          1 |     N =    4485
         between |             .4045199          0          1 |     n =      11
         within  |                    0   .1723523   .1723523 | T-bar = 407.727

.   reg anyviolence personalist v2xpa_popul $dvar if loser==0 & jan1persparty<$cut 
>  

      Source |       SS           df       MS      Number of obs   =     4,485
-------------+----------------------------------   F(11, 4473)     =      2.54
       Model |  1.26825797        11  .115296179   Prob > F        =    0.0034
    Residual |  203.425165     4,473  .045478463   R-squared       =    0.0062
-------------+----------------------------------   Adj R-squared   =    0.0038
       Total |  204.693423     4,484  .045649737   Root MSE        =    .21326

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |  -.0134866    .008771    -1.54   0.124    -.0306822     .003709
 v2xpa_popul |   .0310898   .0357751     0.87   0.385     -.039047    .1012267
        xage |  -.0059085   .0026631    -2.22   0.027    -.0111295   -.0006874
      female |  -.0089421   .0064723    -1.38   0.167     -.021631    .0037469
    educ_med |   -.015707   .0085083    -1.85   0.065    -.0323876    .0009735
   educ_high |  -.0084167   .0089255    -0.94   0.346    -.0259152    .0090817
emp_fulltime |  -.0212273   .0087308    -2.43   0.015    -.0383439   -.0041106
 emp_retired |  -.0180365   .0110469    -1.63   0.103    -.0396939     .003621
   emp_unemp |  -.0046736   .0210187    -0.22   0.824    -.0458806    .0365335
  income_med |   -.007309   .0081238    -0.90   0.368    -.0232357    .0086176
  incom_high |  -.0089119   .0085993    -1.04   0.300    -.0257708     .007947
       _cons |   .1109073   .0180867     6.13   0.000     .0754484    .1463663
------------------------------------------------------------------------------

.                 lincom personalist

 ( 1)  personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0134866    .008771    -1.54   0.124    -.0306822     .003709
------------------------------------------------------------------------------

.                 qui replace b = r(estimate) if n==1

.                 qui replace hi = r(ub) if n==1

.                 qui replace lo = r(lb) if n==1   

.   mixed anyviolence personalist v2xpa_popul $dvar if loser==1 & jan1persparty<$cu
> t ||country: 

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood =   373.5406  
Iteration 1:  Log likelihood =   373.5406  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  9,637
Group variable: country                              Number of groups =     11
                                                     Obs per group:
                                                                  min =    221
                                                                  avg =  876.1
                                                                  max =  1,987
                                                     Wald chi2(11)    =  73.76
Log likelihood =   373.5406                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |  -.0092342    .012757    -0.72   0.469    -.0342374     .015769
 v2xpa_popul |   .0440875   .0110512     3.99   0.000     .0224274    .0657475
        xage |  -.0081175   .0019222    -4.22   0.000     -.011885   -.0043499
      female |  -.0182657   .0048128    -3.80   0.000    -.0276985   -.0088328
    educ_med |  -.0111201   .0069692    -1.60   0.111    -.0247795    .0025394
   educ_high |   -.008014   .0068738    -1.17   0.244    -.0214864    .0054584
emp_fulltime |   .0064887   .0061762     1.05   0.293    -.0056165    .0185938
 emp_retired |   .0109125   .0084971     1.28   0.199    -.0057416    .0275666
   emp_unemp |   .0130967   .0134578     0.97   0.330      -.01328    .0394734
  income_med |  -.0146729   .0060517    -2.42   0.015     -.026534   -.0028118
  incom_high |   -.017369   .0064524    -2.69   0.007    -.0300154   -.0047226
       _cons |    .116248    .017254     6.74   0.000     .0824307    .1500652
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0015749    .000714      .0006476    .0038298
-----------------------------+------------------------------------------------
               var(Residual) |   .0539876   .0007782      .0524837    .0555346
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 176.70        Prob >= chibar2 = 0.0000

.                 xtsum personalist if e(sample),i(cowcode)

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
person~t overall |   .051157   .2203293          0          1 |     N =    9637
         between |             .0935827          0   .2651113 |     n =      11
         within  |             .2018427  -.2139543   .9269984 | T-bar = 876.091

.                 lincom personalist                                               
>                 

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0092342    .012757    -0.72   0.469    -.0342374     .015769
------------------------------------------------------------------------------

.                 qui replace b = r(estimate) if n==4

.                 qui replace hi = r(ub) if n==4

.                 qui replace lo = r(lb) if n==4   

.   mixed anyviolence personalist v2xpa_popul $dvar if loser==0 & jan1persparty>$cu
> t ||country: 

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -116.76472  
Iteration 1:  Log likelihood = -116.76472  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  5,265
Group variable: country                              Number of groups =     16
                                                     Obs per group:
                                                                  min =     49
                                                                  avg =  329.1
                                                                  max =    692
                                                     Wald chi2(11)    =  29.68
Log likelihood = -116.76472                          Prob > chi2      = 0.0018

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0175998   .0303244     0.58   0.562    -.0418348    .0770345
 v2xpa_popul |  -.0572217   .0505914    -1.13   0.258    -.1563789    .0419356
        xage |  -.0041741   .0028581    -1.46   0.144    -.0097758    .0014277
      female |  -.0127306    .006983    -1.82   0.068     -.026417    .0009558
    educ_med |  -.0072643   .0091632    -0.79   0.428    -.0252239    .0106953
   educ_high |  -.0137153   .0111086    -1.23   0.217    -.0354877    .0080572
emp_fulltime |   .0014047        .01     0.14   0.888     -.018195    .0210044
 emp_retired |  -.0261795   .0127316    -2.06   0.040    -.0511329    -.001226
   emp_unemp |  -.0164869   .0131134    -1.26   0.209    -.0421887     .009215
  income_med |   -.006124   .0083076    -0.74   0.461    -.0224065    .0101586
  incom_high |  -.0177404   .0092384    -1.92   0.055    -.0358472    .0003665
       _cons |   .1412854   .0300619     4.70   0.000     .0823651    .2002056
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |    .001979   .0008076      .0008893    .0044037
-----------------------------+------------------------------------------------
               var(Residual) |   .0607725   .0011863      .0584912    .0631428
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 97.60         Prob >= chibar2 = 0.0000

.                 xtsum personalist if e(sample),i(cowcode)

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
person~t overall |  .8070275   .3946691          0          1 |     N =    5265
         between |             .4577377          0          1 |     n =      15
         within  |                    0   .8070275   .8070275 | T-bar =     351

.   reg anyviolence personalist v2xpa_popul $dvar if loser==0 & jan1persparty>$cut

      Source |       SS           df       MS      Number of obs   =     5,265
-------------+----------------------------------   F(11, 5253)     =      4.06
       Model |  2.78843046        11  .253493678   Prob > F        =    0.0000
    Residual |  328.275197     5,253  .062492899   R-squared       =    0.0084
-------------+----------------------------------   Adj R-squared   =    0.0063
       Total |  331.063628     5,264  .062892027   Root MSE        =    .24999

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0135228   .0096359     1.40   0.161    -.0053676    .0324133
 v2xpa_popul |   -.060807   .0142984    -4.25   0.000    -.0888377   -.0327763
        xage |  -.0043989   .0028699    -1.53   0.125    -.0100251    .0012272
      female |  -.0169266   .0070241    -2.41   0.016    -.0306967   -.0031566
    educ_med |  -.0121038   .0090743    -1.33   0.182    -.0298932    .0056857
   educ_high |  -.0241283   .0111046    -2.17   0.030     -.045898   -.0023586
emp_fulltime |   .0035701   .0099707     0.36   0.720    -.0159767    .0231169
 emp_retired |   -.019547   .0126307    -1.55   0.122    -.0443083    .0052144
   emp_unemp |  -.0308771   .0129503    -2.38   0.017    -.0562652    -.005489
  income_med |  -.0091613   .0083866    -1.09   0.275    -.0256026      .00728
  incom_high |  -.0217338   .0091974    -2.36   0.018    -.0397645   -.0037031
       _cons |   .1493318   .0189088     7.90   0.000     .1122627     .186401
------------------------------------------------------------------------------

.                 lincom personalist

 ( 1)  personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0135228   .0096359     1.40   0.161    -.0053676    .0324133
------------------------------------------------------------------------------

.                 qui replace b = r(estimate) if n==2

.                 qui replace hi = r(ub) if n==2

.                 qui replace lo = r(lb) if n==2   

.   mixed anyviolence personalist v2xpa_popul $dvar if loser==1 & jan1persparty>$cu
> t ||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood =   -819.063  
Iteration 1:  Log likelihood =   -819.063  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    =  8,620
Group variable: country                              Number of groups =     17
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  507.1
                                                                  max =    908
                                                     Wald chi2(11)    =  89.11
Log likelihood =   -819.063                          Prob > chi2      = 0.0000

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 personalist |   .0217564   .0084548     2.57   0.010     .0051852    .0383276
 v2xpa_popul |   .0272435   .0145148     1.88   0.061     -.001205     .055692
        xage |  -.0071574   .0023985    -2.98   0.003    -.0118583   -.0024565
      female |  -.0231325   .0058426    -3.96   0.000    -.0345837   -.0116812
    educ_med |  -.0098193   .0078225    -1.26   0.209    -.0251511    .0055126
   educ_high |  -.0200759   .0092202    -2.18   0.029    -.0381473   -.0020046
emp_fulltime |   .0035647   .0080883     0.44   0.659    -.0122881    .0194175
 emp_retired |    -.01877   .0106798    -1.76   0.079    -.0397019     .002162
   emp_unemp |  -.0158045   .0111948    -1.41   0.158    -.0377458    .0061369
  income_med |  -.0009372    .007039    -0.13   0.894    -.0147333     .012859
  incom_high |  -.0191562   .0075726    -2.53   0.011    -.0339982   -.0043141
       _cons |   .1253849   .0207773     6.03   0.000     .0846622    .1661076
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0031739   .0011497      .0015605    .0064555
-----------------------------+------------------------------------------------
               var(Residual) |   .0703769   .0010731      .0683049    .0725118
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 306.03        Prob >= chibar2 = 0.0000

.                 xtsum personalist if e(sample),i(cowcode)

Variable         |      Mean   Std. dev.       Min        Max |    Observations
-----------------+--------------------------------------------+----------------
person~t overall |  .3647332   .4813832          0          1 |     N =    8620
         between |             .3000639          0          1 |     n =      16
         within  |             .3718238  -.5237284   1.251719 | T-bar =  538.75

.                 lincom personalist                                               
>                /* non-personalist loser; reference==nonpers winner */

 ( 1)  [anyviolence]personalist = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0217564   .0084548     2.57   0.010     .0051852    .0383276
------------------------------------------------------------------------------

.                 qui replace b = r(estimate) if n==5

.                 qui replace hi = r(ub) if n==5

.                 qui replace lo = r(lb) if n==5   

.   gen rb=round(b,.001)
(28,340 missing values generated)

.   replace b=. if n==3
(1 real change made, 1 to missing)

.   replace hi=. if n==3
(1 real change made, 1 to missing)

.   replace lo=. if n==3
(1 real change made, 1 to missing)

.   twoway (rspike lo hi n if n<=5,lcol(blue*.45))(scatter b n if n<=5,mlab(rb)mcol
> (blue)msy(O)yline(0,lcol(gs12)lp(dash)) ///
>                 xscale(range(0.7 5.3)) ///
>                 xlab(1 `""Non-personalist" "ruling party"  "Winners""' ///
>                 2 `""{bf:Personalist}" "ruling party"  "Winners""'  ///
>                 4 `""Non-personalist" "ruling party"  "Losers""'  ///
>                 5 `""{bf:Personalist}" "ruling party"  "{bf:Losers}""') ///
>                 xtit(" ")legend(off) ytit("Marginal effect of personalist party V
> oter",size(medium))ylab(-.08(.04).04) ///
>                 tit("Justifying political violence",size(medium)))

.         
. * Check with ordered outcome variable *
.   tab polviolence

  RECODE of |
   v162 (do |
        you |
   justify: |
  political |
   violence |
    (Q44N)) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     22,174       78.10       78.10
          2 |      2,595        9.14       87.25
          3 |      1,217        4.29       91.53
          4 |        541        1.91       93.44
          5 |        794        2.80       96.23
          6 |        369        1.30       97.53
          7 |        232        0.82       98.35
          8 |        159        0.56       98.91
          9 |        106        0.37       99.28
         10 |        203        0.72      100.00
------------+-----------------------------------
      Total |     28,390      100.00

.   meologit polviolence C.personalist##C.jan1pers $dvar if lose==1 || country:

Fitting fixed-effects model:

Iteration 0:  Log likelihood = -16941.792  
Iteration 1:  Log likelihood =   -16738.9  
Iteration 2:  Log likelihood = -16736.163  
Iteration 3:  Log likelihood = -16736.162  

Refining starting values:

Grid node 0:  Log likelihood = -16200.243

Fitting full model:

Iteration 0:  Log likelihood = -16200.243  (not concave)
Iteration 1:  Log likelihood = -16197.719  (backed up)
Iteration 2:  Log likelihood = -16174.359  
Iteration 3:  Log likelihood =  -16148.42  
Iteration 4:  Log likelihood = -16148.277  
Iteration 5:  Log likelihood = -16148.277  

Mixed-effects ologit regression                 Number of obs     =     18,257
Group variable: country                         Number of groups  =         28

                                                Obs per group:
                                                              min =        131
                                                              avg =      652.0
                                                              max =      1,987

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(12)     =     292.64
Log likelihood = -16148.277                     Prob > chi2       =     0.0000
---------------------------------------------------------------------------------
    polviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |  -.1124596   .1706688    -0.66   0.510    -.4469642    .2220451
  jan1persparty |   .1301743   .5861163     0.22   0.824    -1.018593    1.278941
                |
  c.personalist#|
c.jan1persparty |   .3731432   .2627574     1.42   0.156     -.141852    .8881383
                |
           xage |  -.1541072   .0147453   -10.45   0.000    -.1830074   -.1252071
         female |   -.325468   .0369097    -8.82   0.000    -.3978096   -.2531263
       educ_med |  -.0643189   .0519012    -1.24   0.215    -.1660433    .0374056
      educ_high |  -.1010789    .054745    -1.85   0.065    -.2083771    .0062193
   emp_fulltime |  -.0770058   .0474533    -1.62   0.105    -.1700126    .0160011
    emp_retired |  -.0323568   .0678434    -0.48   0.633    -.1653274    .1006137
      emp_unemp |  -.0504596   .0842831    -0.60   0.549    -.2156515    .1147324
     income_med |  -.0097549   .0452052    -0.22   0.829    -.0983554    .0788457
     incom_high |  -.1402821    .048211    -2.91   0.004    -.2347739   -.0457904
----------------+----------------------------------------------------------------
          /cut1 |   .3771624   .3227134                     -.2553443    1.009669
          /cut2 |   1.072828   .3228715                      .4400115    1.705644
          /cut3 |   1.554546   .3231359                      .9212112    2.187881
          /cut4 |   1.833774   .3233812                      1.199958    2.467589
          /cut5 |   2.424412   .3242343                      1.788924    3.059899
          /cut6 |   2.890275   .3253836                      2.252535    3.528016
          /cut7 |   3.334666   .3271033                      2.693555    3.975777
          /cut8 |   3.824859   .3301143                      3.177847    4.471872
          /cut9 |    4.24394   .3341232                       3.58907    4.898809
----------------+----------------------------------------------------------------
country         |
      var(_cons)|   .5197064   .1447528                       .301075    .8971014
---------------------------------------------------------------------------------
LR test vs. ologit model: chibar2(01) = 1175.77       Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [polviolence]personalist + .2*[polviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 polviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0378309   .1227464    -0.31   0.758    -.2784095    .2027476
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [polviolence]personalist + .7*[polviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 polviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .1487406   .0638617     2.33   0.020     .0235739    .2739074
------------------------------------------------------------------------------

.  
. * Additional specification for interaction term *
.   mixed anyviolence C.personalist##C.jan1pers if loser==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -629.11158  
Iteration 1:  Log likelihood = -629.11158  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,325
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  654.5
                                                                  max =  1,988
                                                     Wald chi2(3)     =  21.77
Log likelihood = -629.11158                          Prob > chi2      = 0.0001

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0058177   .0179327     0.32   0.746    -.0293297    .0409651
  jan1persparty |   .0208095    .042594     0.49   0.625    -.0626731    .1042921
                |
  c.personalist#|
c.jan1persparty |   .0359083   .0282318     1.27   0.203    -.0194249    .0912416
                |
          _cons |   .0547492   .0226352     2.42   0.016     .0103851    .0991133
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026833   .0007539      .0015471    .0046539
-----------------------------+------------------------------------------------
               var(Residual) |    .062403   .0006524      .0611373    .0636949
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 576.13        Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [anyviolence]personalist + .2*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0129993   .0127829     1.02   0.309    -.0120547    .0380534
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [anyviolence]personalist + .7*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0309535   .0068498     4.52   0.000     .0175282    .0443788
------------------------------------------------------------------------------

.    mixed anyviolence C.personalist##C.jan1pers native  leftid rightid  $dvar if l
> oser==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -540.29608  
Iteration 1:  Log likelihood = -540.29608  (backed up)

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(15)    = 137.10
Log likelihood = -540.29608                          Prob > chi2      = 0.0000

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |   .0032121    .017926     0.18   0.858    -.0319222    .0383463
  jan1persparty |   .0282923   .0420231     0.67   0.501    -.0540715    .1106561
                |
  c.personalist#|
c.jan1persparty |   .0346314   .0281445     1.23   0.219    -.0205309    .0897937
                |
         native |  -.0156562   .0076749    -2.04   0.041    -.0306988   -.0006136
         leftid |  -.0014447   .0048867    -0.30   0.768    -.0110225    .0081332
        rightid |   .0004407   .0047752     0.09   0.926    -.0089186    .0097999
           xage |  -.0078358   .0015101    -5.19   0.000    -.0107955    -.004876
         female |  -.0213626   .0037583    -5.68   0.000    -.0287287   -.0139966
       educ_med |  -.0101956   .0052105    -1.96   0.050     -.020408    .0000168
      educ_high |  -.0147266    .005584    -2.64   0.008     -.025671   -.0037823
   emp_fulltime |   .0052789   .0049654     1.06   0.288    -.0044532     .015011
    emp_retired |    -.00337   .0067109    -0.50   0.616     -.016523    .0097831
      emp_unemp |  -.0037723   .0083606    -0.45   0.652    -.0201587    .0126141
     income_med |  -.0078469   .0046161    -1.70   0.089    -.0168943    .0012005
     incom_high |  -.0186607   .0049329    -3.78   0.000    -.0283291   -.0089924
          _cons |   .1344475   .0250889     5.36   0.000     .0852741    .1836209
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0026045   .0007348      .0014982    .0045278
-----------------------------+------------------------------------------------
               var(Residual) |   .0618156   .0006475      .0605595    .0630978
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 538.63        Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [anyviolence]personalist + .2*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0101384   .0128026     0.79   0.428    -.0149542     .035231
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [anyviolence]personalist + .7*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0274541   .0068964     3.98   0.000     .0139373    .0409708
------------------------------------------------------------------------------

.   mixed anyviolence C.personalist##C.jan1pers v2paminor v2xpa_popul v2paviol $dva
> r if loser==1||country:

Performing EM optimization ...

Performing gradient-based optimization: 
Iteration 0:  Log likelihood = -530.25833  
Iteration 1:  Log likelihood = -530.25833  

Computing standard errors ...

Mixed-effects ML regression                          Number of obs    = 18,257
Group variable: country                              Number of groups =     28
                                                     Obs per group:
                                                                  min =    131
                                                                  avg =  652.0
                                                                  max =  1,987
                                                     Wald chi2(15)    = 157.46
Log likelihood = -530.25833                          Prob > chi2      = 0.0000

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |  -.0117179   .0186727    -0.63   0.530    -.0483157    .0248798
  jan1persparty |   .0279842   .0399467     0.70   0.484    -.0503099    .1062783
                |
  c.personalist#|
c.jan1persparty |   .0410207   .0287878     1.42   0.154    -.0154025    .0974438
                |
      v2paminor |  -.0079857   .0029324    -2.72   0.006    -.0137331   -.0022383
    v2xpa_popul |   .0211571   .0127245     1.66   0.096    -.0037826    .0460967
       v2paviol |   .0045851   .0043323     1.06   0.290    -.0039061    .0130763
           xage |   -.007808   .0015115    -5.17   0.000    -.0107704   -.0048455
         female |  -.0204089   .0037488    -5.44   0.000    -.0277564   -.0130614
       educ_med |  -.0101484   .0052038    -1.95   0.051    -.0203477    .0000509
      educ_high |  -.0121374    .005582    -2.17   0.030    -.0230779    -.001197
   emp_fulltime |   .0047642   .0049656     0.96   0.337    -.0049682    .0144966
    emp_retired |  -.0035128   .0067102    -0.52   0.601    -.0166646    .0096389
      emp_unemp |  -.0038459   .0083547    -0.46   0.645    -.0202207     .012529
     income_med |  -.0080161   .0046125    -1.74   0.082    -.0170565    .0010243
     incom_high |  -.0180834   .0049264    -3.67   0.000    -.0277389   -.0084279
          _cons |   .1178532   .0249351     4.73   0.000     .0689813    .1667251
---------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects parameters  |   Estimate   Std. err.     [95% conf. interval]
-----------------------------+------------------------------------------------
country: Identity            |
                  var(_cons) |   .0023126   .0006739      .0013063    .0040939
-----------------------------+------------------------------------------------
               var(Residual) |   .0617583   .0006469      .0605033    .0630393
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 379.93        Prob >= chibar2 = 0.0000

.   lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  [anyviolence]personalist + .2*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0035138   .0134775    -0.26   0.794    -.0299293    .0229017
------------------------------------------------------------------------------

.   lincom personalist + c.personalist#c.jan1persparty*.7

 ( 1)  [anyviolence]personalist + .7*[anyviolence]c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0169965   .0073582     2.31   0.021     .0025747    .0314184
------------------------------------------------------------------------------

.   
.  * Check against populist voter effect *
.   reghdfe anyviolence C.personalist##C.jan1pers populism   $dvar if loser==1 & no
> var==0,a(country)
note: jan1persparty is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: jan1persparty omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,447
Absorbing 1 HDFE group                            F(  12,   9419) =       9.90
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0509
                                                  Adj R-squared   =     0.0482
                                                  Within R-sq.    =     0.0125
                                                  Root MSE        =     0.2753

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |  -.0263712   .0220284    -1.20   0.231    -.0695517    .0168092
  jan1persparty |          0  (omitted)
                |
  c.personalist#|
c.jan1persparty |   .0584009   .0321911     1.81   0.070    -.0047006    .1215024
                |
       populism |   .0512771   .0154038     3.33   0.001     .0210824    .0814718
           xage |  -.0077656   .0023335    -3.33   0.001    -.0123397   -.0031914
         female |   -.029558   .0057927    -5.10   0.000     -.040913   -.0182031
       educ_med |  -.0138473   .0078785    -1.76   0.079    -.0292908    .0015961
      educ_high |  -.0229934   .0086414    -2.66   0.008    -.0399324   -.0060544
   emp_fulltime |   .0081725   .0076597     1.07   0.286    -.0068421    .0231871
    emp_retired |  -.0138982   .0102795    -1.35   0.176    -.0340482    .0062519
      emp_unemp |  -.0143481   .0128355    -1.12   0.264    -.0395084    .0108122
     income_med |  -.0047899   .0069367    -0.69   0.490    -.0183873    .0088075
     incom_high |  -.0273342    .007495    -3.65   0.000     -.042026   -.0126424
          _cons |    .146272   .0154248     9.48   0.000     .1160361    .1765079
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        16           0          16     |
-----------------------------------------------------+

.           lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  personalist + .2*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0146911   .0162602    -0.90   0.366    -.0465645    .0171824
------------------------------------------------------------------------------

.           lincom personalist + c.personalist#c.jan1persparty*.7  

 ( 1)  personalist + .7*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0145094   .0086568     1.68   0.094    -.0024599    .0314786
------------------------------------------------------------------------------

.   reghdfe anyviolence C.personalist##C.jan1pers C.populism##C.jan1pers $dvar if l
> oser==1 & novar==0,a(country)
note: jan1persparty is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
note: jan1persparty is probably collinear with the fixed effects (all partialled-ou
> t values are close to zero; tol = 1.0e-09)
(MWFE estimator converged in 1 iterations)
note: jan1persparty omitted because of collinearity
note: jan1persparty omitted because of collinearity

HDFE Linear regression                            Number of obs   =      9,447
Absorbing 1 HDFE group                            F(  13,   9418) =       9.20
                                                  Prob > F        =     0.0000
                                                  R-squared       =     0.0510
                                                  Adj R-squared   =     0.0482
                                                  Within R-sq.    =     0.0125
                                                  Root MSE        =     0.2753

---------------------------------------------------------------------------------
    anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
----------------+----------------------------------------------------------------
    personalist |  -.0410452   .0277163    -1.48   0.139    -.0953751    .0132846
  jan1persparty |          0  (omitted)
                |
  c.personalist#|
c.jan1persparty |   .0815596   .0417253     1.95   0.051     -.000231    .1633501
                |
       populism |   .0843008   .0408684     2.06   0.039     .0041899    .1644117
  jan1persparty |          0  (omitted)
                |
     c.populism#|
c.jan1persparty |   -.057408   .0658055    -0.87   0.383    -.1864011     .071585
                |
           xage |  -.0077609   .0023335    -3.33   0.001    -.0123351   -.0031868
         female |  -.0295303   .0057929    -5.10   0.000    -.0408855    -.018175
       educ_med |  -.0135198   .0078875    -1.71   0.087     -.028981    .0019414
      educ_high |  -.0224019   .0086681    -2.58   0.010    -.0393932   -.0054106
   emp_fulltime |   .0080581   .0076609     1.05   0.293    -.0069588    .0230751
    emp_retired |  -.0140318   .0102808    -1.36   0.172    -.0341844    .0061207
      emp_unemp |  -.0144927   .0128367    -1.13   0.259    -.0396554      .01067
     income_med |  -.0047789   .0069368    -0.69   0.491    -.0183765    .0088186
     incom_high |  -.0270474   .0075023    -3.61   0.000    -.0417535   -.0123413
          _cons |   .1467166   .0154334     9.51   0.000     .1164638    .1769694
---------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
     country |        16           0          16     |
-----------------------------------------------------+

.           lincom personalist + c.personalist#c.jan1persparty*.2

 ( 1)  personalist + .2*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |  -.0247333   .0199226    -1.24   0.214    -.0637858    .0143192
------------------------------------------------------------------------------

.           lincom personalist + c.personalist#c.jan1persparty*.7  

 ( 1)  personalist + .7*c.personalist#c.jan1persparty = 0

------------------------------------------------------------------------------
 anyviolence | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
         (1) |   .0160465   .0088344     1.82   0.069    -.0012709    .0333638
------------------------------------------------------------------------------

.           
.  * Can't calculate bandwidth via cross-validation with perfect collinear between 
> interaction and FE *
.  forval i =1/6 {
  2.         local i =`i'/10
  3.         interflex anyviolence personalist jan1persparty v2xpa_popul $dvar if l
> oser==1 & novar==0,fe(country)type(kernel)bw(`i')
  4.         mat list r(margeff)
  5.  }
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.00533207   .01519533  -.03511436   .02445022
 r2   .22771729  -.01089128   .01389489  -.03812477   .01634222
 r3   .24380651  -.01583864   .01273366  -.04079615   .00911887
 r4   .25989573  -.01987681   .01172794  -.04286316   .00310954
 r5   .27598496   -.0227213   .01090107  -.04408701  -.00135558
 r6   .29207418  -.02418949   .01027312  -.04432444  -.00405454
 r7    .3081634   -.0242902   .00984814  -.04359221   -.0049882
 r8   .32425263  -.02326347   .00960553  -.04208998  -.00443697
 r9   .34034185  -.02152919   .00950211  -.04015299   -.0029054
r10   .35643107  -.01954682   .00948438  -.03813587  -.00095778
r11    .3725203  -.01764437   .00950344  -.03627076   .00098202
r12   .38860952  -.01590155    .0095248  -.03456981   .00276671
r13   .40469874  -.01414514    .0095307  -.03282498   .00453469
r14   .42078797  -.01205065   .00951662  -.03070287   .00660158
r15   .43687719   -.0092938   .00948527  -.02788459   .00929699
r16   .45296641  -.00568982    .0094412  -.02419423   .01281459
r17   .46905564  -.00128213   .00938719  -.01968068   .01711643
r18   .48514486   .00363656   .00932291    -.014636   .02190912
r19   .50123408   .00857775     .009245  -.00954211   .02669761
r20    .5173233   .01297759   .00914779  -.00495175   .03090693
r21   .53341253   .01634992   .00902413  -.00133705   .03403688
r22   .54950175   .01841673   .00886659   .00103853   .03579494
r23   .56559097   .01916715    .0086699   .00217445   .03615985
r24    .5816802   .01882739   .00843476   .00229556   .03535921
r25   .59776942   .01776332   .00817214   .00174622   .03378041
r26   .61385864   .01636336   .00790582   .00086824   .03185847
r27   .62994787   .01495177   .00767083  -.00008277   .02998631
r28   .64603709   .01375885    .0075068  -.00095422   .02847192
r29   .66212631   .01293965   .00744739  -.00165698   .02753627
r30   .67821554   .01261092   .00750936  -.00210715   .02732899
r31   .69430476   .01287669   .00768662  -.00218882   .02794219
r32   .71039398   .01382802   .00795255  -.00175869   .02941474
r33   .72648321   .01552017   .00826884  -.00068646   .03172679
r34   .74257243   .01794207   .00859617   .00109389   .03479026
r35   .75866165     .020997   .00890232   .00354876   .03844524
r36   .77475088   .02450678    .0091661   .00654155   .04247201
r37    .7908401   .02824031   .00937742    .0098609   .04661973
r38   .80692932   .03195566   .00953491   .01326758   .05064373
r39   .82301855    .0354405   .00964232    .0165419   .05433911
r40   .83910777   .03853892   .00970523   .01951702   .05756082
r41   .85519699   .04115866   .00972851   .02209112   .06022619
r42   .87128621    .0432599   .00971528   .02421831    .0623015
r43   .88737544   .04483057   .00966708   .02588343    .0637777
r44   .90346466   .04585556   .00958538   .02706856   .06464257
r45   .91955388   .04628838   .00947384   .02771999   .06485677
r46   .93564311   .04603303   .00934111   .02772478   .06434127
r47   .95173233   .04494264   .00920363   .02690386   .06298142
r48   .96782155   .04283666   .00908786   .02502479   .06064853
r49   .98391078   .03953285   .00903115   .02183211   .05723358
r50           1   .03488447   .00908018   .01708764    .0526813
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.02328051   .01355285  -.04984361   .00328259
 r2   .22771729  -.02200173   .01302832  -.04753677   .00353332
 r3   .24380651  -.02063691    .0125618   -.0452576   .00398377
 r4   .25989573  -.01920053   .01214653   -.0430073   .00460623
 r5   .27598496  -.01770554    .0117761  -.04078628    .0053752
 r6   .29207418  -.01616372   .01144457  -.03859465   .00626722
 r7    .3081634  -.01458605   .01114648  -.03643276   .00726065
 r8   .32425263  -.01298307   .01087698  -.03430155   .00833541
 r9   .34034185  -.01136512   .01063172   -.0322029   .00947267
r10   .35643107  -.00974257   .01040693  -.03013977   .01065464
r11    .3725203  -.00812597   .01019934  -.02811631   .01186437
r12   .38860952  -.00652604   .01000614  -.02613771   .01308564
r13   .40469874  -.00495353   .00982494  -.02421006     .014303
r14   .42078797  -.00341905   .00965373  -.02234001   .01550191
r15   .43687719  -.00193268   .00949082  -.02053434   .01666898
r16   .45296641   -.0005036   .00933484  -.01879956   .01779236
r17   .46905564   .00086038   .00918473  -.01714136   .01886212
r18   .48514486   .00215333   .00903972  -.01556419   .01987085
r19   .50123408   .00337171   .00889937  -.01407072   .02081415
r20    .5173233   .00451486   .00876363  -.01266154   .02169126
r21   .53341253   .00558542    .0086329  -.01133476    .0225056
r22   .54950175   .00658969   .00850807  -.01008583   .02326521
r23   .56559097   .00753785   .00839062  -.00890745   .02398316
r24    .5816802   .00844401   .00828261  -.00778961   .02467762
r25   .59776942   .00932593   .00818674  -.00671979   .02537165
r26   .61385864   .01020459   .00810624  -.00568335   .02609253
r27   .62994787   .01110326   .00804471  -.00466408    .0268706
r28   .64603709   .01204632   .00800588  -.00364492   .02773757
r29   .66212631   .01305781   .00799328  -.00260872   .02872434
r30   .67821554   .01415975   .00800982  -.00153921   .02985871
r31   .69430476   .01537056   .00805749  -.00042184   .03116296
r32   .71039398   .01670363   .00813705   .00075531   .03265196
r33   .72648321   .01816637   .00824789    .0020008   .03433194
r34   .74257243    .0197597   .00838814   .00331924   .03620016
r35   .75866165   .02147822   .00855485   .00471102   .03824541
r36   .77475088   .02331085   .00874432   .00617229   .04044941
r37    .7908401   .02524187   .00895253   .00769525    .0427885
r38   .80692932    .0272522   .00917543   .00926869   .04523571
r39   .82301855   .02932067   .00940932   .01087875    .0477626
r40   .83910777   .03142526   .00965099   .01250967   .05034086
r41   .85519699   .03354405   .00989791   .01414451    .0529436
r42   .87128621   .03565596   .01014824   .01576577   .05554615
r43   .88737544   .03774119   .01040087   .01735586   .05812653
r44   .90346466   .03978148   .01065534   .01889739   .06066557
r45   .91955388   .04176012   .01091182   .02037335   .06314689
r46   .93564311   .04366185     .011171   .02176709   .06555661
r47   .95173233   .04547266   .01143408   .02306228   .06788304
r48   .96782155   .04717954   .01170264   .02424278    .0701163
r49   .98391078   .04877022   .01197865   .02529249   .07224795
r50           1   .05023288   .01226436   .02619518   .07427058
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.01579378   .01413478  -.04349744   .01190988
 r2   .22771729  -.01466531   .01374345  -.04160198   .01227136
 r3   .24380651  -.01354437   .01336818  -.03974551   .01265678
 r4   .25989573  -.01243236    .0130081  -.03792777   .01306305
 r5   .27598496  -.01133053   .01266244  -.03614845    .0134874
 r6   .29207418   -.0102399   .01233047  -.03440718   .01392737
 r7    .3081634  -.00916135   .01201154  -.03270354   .01438084
 r8   .32425263  -.00809551   .01170508  -.03103705   .01484602
 r9   .34034185  -.00704282   .01141058  -.02940715   .01532152
r10   .35643107  -.00600346   .01112764  -.02781324   .01580632
r11    .3725203   -.0049774   .01085594  -.02625464   .01629985
r12   .38860952  -.00396433   .01059524  -.02473062   .01680195
r13   .40469874  -.00296371   .01034542  -.02324037   .01731294
r14   .42078797  -.00197471   .01010648  -.02178304   .01783362
r15   .43687719  -.00099624   .00987851  -.02035776   .01836528
r16   .45296641  -.00002694   .00966173  -.01896359   .01890971
r17   .46905564   .00093484   .00945651  -.01759958   .01946926
r18   .48514486   .00189099   .00926332  -.01626479   .02004677
r19   .50123408   .00284367   .00908279  -.01495827   .02064561
r20    .5173233   .00379529   .00891566  -.01367909   .02126966
r21   .53341253   .00474847   .00876282  -.01242633   .02192328
r22   .54950175   .00570605   .00862525  -.01119914   .02261124
r23   .56559097   .00667104   .00850408  -.00999665   .02333873
r24    .5816802    .0076466   .00840048  -.00881803   .02411123
r25   .59776942   .00863597   .00831568  -.00766246   .02493441
r26   .61385864    .0096425   .00825095  -.00652907   .02581407
r27   .62994787   .01066951   .00820752  -.00541694   .02675596
r28   .64603709   .01172033   .00818656  -.00432503   .02776569
r29   .66212631   .01279818    .0081891  -.00325217   .02884853
r30   .67821554   .01390614   .00821605  -.00219701    .0300093
r31   .69430476   .01504713   .00826807    -.001158   .03125225
r32   .71039398   .01622377   .00834563  -.00013336   .03258089
r33   .72648321   .01743842    .0084489   .00087887   .03399797
r34   .74257243   .01869308   .00857784   .00188083   .03550533
r35   .75866165   .01998935   .00873209   .00287476   .03710394
r36   .77475088   .02132841   .00891111   .00386296   .03879385
r37    .7908401   .02271095   .00911409   .00484766   .04057424
r38   .80692932   .02413721   .00934008   .00583099   .04244344
r39   .82301855   .02560691   .00958798   .00681482     .044399
r40   .83910777   .02711926   .00985657   .00780074   .04643778
r41   .85519699   .02867297   .01014458   .00878995   .04855598
r42   .87128621   .03026626   .01045072   .00978323    .0507493
r43   .88737544    .0318969   .01077368   .01078088   .05301292
r44   .90346466   .03356219    .0111122   .01178268   .05534169
r45   .91955388   .03525903   .01146505   .01278794   .05773012
r46   .93564311   .03698396   .01183109   .01379545   .06017248
r47   .95173233   .03873321   .01220926    .0148035   .06266291
r48   .96782155   .04050269   .01259857   .01580995   .06519542
r49   .98391078   .04228811   .01299814   .01681223   .06776399
r50           1   .04408499   .01340719   .01780738    .0703626
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.01371737   .01470701  -.04254258   .01510785
 r2   .22771729  -.01278961   .01431868   -.0408537   .01527449
 r3   .24380651  -.01186528   .01393882  -.03918487   .01545431
 r4   .25989573  -.01094416   .01356748  -.03753592   .01564761
 r5   .27598496  -.01002595   .01320471   -.0359067    .0158548
 r6   .29207418  -.00911033   .01285062  -.03429708   .01607643
 r7    .3081634  -.00819691   .01250538    -.032707   .01631319
 r8   .32425263  -.00728527    .0121692  -.03113647   .01656592
 r9   .34034185  -.00637495   .01184235  -.02958554   .01683564
r10   .35643107  -.00546542   .01152517  -.02805434    .0171235
r11    .3725203  -.00455613   .01121805   -.0265431   .01743084
r12   .38860952  -.00364647   .01092146  -.02505214    .0177592
r13   .40469874  -.00273581   .01063595  -.02358189   .01811028
r14   .42078797  -.00182346   .01036215  -.02213289   .01848598
r15   .43687719  -.00090871   .01010075   -.0207058   .01888839
r16   .45296641   9.201e-06   .00985253  -.01930141   .01931981
r17   .46905564   .00093105   .00961838  -.01792063   .01978272
r18   .48514486   .00185764   .00939922  -.01656448   .02027977
r19   .50123408   .00278982   .00919606  -.01523412   .02081377
r20    .5173233   .00372846   .00900998  -.01393078   .02138769
r21   .53341253   .00467441   .00884208  -.01265575   .02200458
r22   .54950175   .00562859   .00869351  -.01141037   .02266755
r23   .56559097   .00659189   .00856539  -.01019596   .02337975
r24    .5816802   .00756523   .00845883  -.00901376   .02414423
r25   .59776942   .00854953   .00837487  -.00786491   .02496396
r26   .61385864   .00954568   .00831445  -.00675034    .0258417
r27   .62994787   .01055461   .00827839  -.00567074   .02677995
r28   .64603709   .01157719   .00826735  -.00462651   .02778089
r29   .66212631   .01261431   .00828179   -.0036177   .02884632
r30   .67821554   .01366682   .00832198  -.00264397   .02997761
r31   .69430476   .01473554   .00838799  -.00170461   .03117569
r32   .71039398   .01582127   .00847964  -.00079851   .03244105
r33   .72648321   .01692477   .00859658   .00007578   .03377375
r34   .74257243   .01804674   .00873827   .00092004   .03517344
r35   .75866165   .01918785   .00890403   .00173628   .03663942
r36   .77475088   .02034872   .00909301   .00252675   .03817069
r37    .7908401    .0215299    .0093043    .0032938   .03976599
r38   .80692932   .02273188   .00953691   .00403989   .04142387
r39   .82301855   .02395509   .00978979   .00476745   .04314273
r40   .83910777   .02519989   .01006191   .00547891   .04492087
r41   .85519699   .02646657   .01035221    .0061766   .04675653
r42   .87128621   .02775533   .01065967   .00686275    .0486479
r43   .88737544   .02906629   .01098329   .00753943   .05059315
r44   .90346466   .03039952   .01132212   .00820856   .05259047
r45   .91955388   .03175496   .01167526   .00887187   .05463804
r46   .93564311   .03313249   .01204184   .00953091   .05673407
r47   .95173233   .03453191   .01242107   .01018705   .05887677
r48   .96782155   .03595292    .0128122   .01084146   .06106437
r49   .98391078   .03739512   .01321453   .01149511   .06329513
r50           1   .03885806   .01362742   .01214881   .06556731
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.01338866   .01507121   -.0429277   .01615038
 r2   .22771729  -.01250847   .01467332  -.04126765   .01625071
 r3   .24380651  -.01162745   .01428179  -.03961926   .01636435
 r4   .25989573  -.01074537    .0138969  -.03798279   .01649206
 r5   .27598496  -.00986197   .01351894  -.03635861   .01663467
 r6   .29207418    -.008977   .01314827  -.03474713   .01679313
 r7    .3081634   -.0080902   .01278528  -.03314888   .01696848
 r8   .32425263  -.00720128   .01243042  -.03156446   .01716189
 r9   .34034185  -.00630998   .01208421  -.02999459   .01737463
r10   .35643107  -.00541599   .01174721   -.0284401   .01760812
r11    .3725203  -.00451902   .01142007  -.02690195   .01786392
r12   .38860952  -.00361875   .01110351  -.02538122   .01814372
r13   .40469874  -.00271488    .0107983  -.02387915    .0184494
r14   .42078797  -.00180708   .01050531  -.02239712   .01878295
r15   .43687719  -.00089503    .0102255  -.02093665   .01914658
r16   .45296641   .00002159   .00995988  -.01949942   .01954261
r17   .46905564   .00094314   .00970956  -.01808725   .01997353
r18   .48514486   .00186994    .0094757  -.01670209   .02044197
r19   .50123408   .00280234   .00925952  -.01534598   .02095066
r20    .5173233   .00374068   .00906228  -.01402106   .02150243
r21   .53341253   .00468532   .00888527   -.0127295   .02210013
r22   .54950175   .00563659   .00872976  -.01147343    .0227466
r23   .56559097   .00659485   .00859697  -.01025491    .0234446
r24    .5816802   .00756043   .00848806  -.00907586   .02419673
r25   .59776942    .0085337   .00840406  -.00793796   .02500536
r26   .61385864   .00951499   .00834585  -.00684258   .02587256
r27   .62994787   .01050465   .00831413  -.00579074   .02680003
r28   .64603709     .011503   .00830935  -.00478302   .02778902
r29   .66212631   .01251039   .00833175  -.00381954   .02884032
r30   .67821554   .01352714   .00838132  -.00289994   .02995423
r31   .69430476   .01455358   .00845779   -.0020234   .03113055
r32   .71039398   .01559001   .00856068  -.00118862   .03236863
r33   .72648321   .01663674   .00868929  -.00039394   .03366743
r34   .74257243   .01769408   .00884274   .00036263   .03502553
r35   .75866165   .01876231   .00902003   .00108337   .03644125
r36   .77475088    .0198417   .00922006   .00177072   .03791269
r37    .7908401   .02093253   .00944165   .00242723   .03943783
r38   .80692932   .02203504   .00968361   .00305552   .04101456
r39   .82301855   .02314949   .00994472    .0036582   .04264077
r40   .83910777   .02427609    .0102238   .00423781   .04431436
r41   .85519699   .02541506   .01051971   .00479681    .0460333
r42   .87128621   .02656659   .01083134   .00533755   .04779564
r43   .88737544   .02773088   .01115768   .00586224   .04959952
r44   .90346466   .02890808   .01149773   .00637294   .05144322
r45   .91955388   .03009834   .01185061   .00687157   .05332511
r46   .93564311    .0313018   .01221547   .00735991   .05524369
r47   .95173233   .03251856   .01259155   .00783957   .05719756
r48   .96782155   .03374872   .01297815   .00831202   .05918541
r49   .98391078   .03499234    .0133746   .00877861   .06120608
r50           1   .03624949   .01378033   .00924055   .06325843
Fixed effects included; clustered standard errors highly recommended

r(margeff)[50,5]
         xlevel        marg          se        CI_l        CI_u
 r1   .21162806  -.01342277   .01530245  -.04341502   .01656947
 r2   .22771729  -.01254508   .01489583  -.04174036    .0166502
 r3   .24380651  -.01166526   .01449482  -.04007459   .01674407
 r4   .25989573  -.01078317   .01409979  -.03841825   .01685191
 r5   .27598496  -.00989867   .01371112  -.03677196   .01697462
 r6   .29207418  -.00901162   .01332924  -.03513645   .01711322
 r7    .3081634  -.00812186   .01295466  -.03351253   .01726881
 r8   .32425263  -.00722926   .01258792  -.03190113   .01744261
 r9   .34034185  -.00633366   .01222962  -.03030328   .01763596
r10   .35643107  -.00543491   .01188045  -.02872015   .01785034
r11    .3725203  -.00453285   .01154113  -.02715305   .01808735
r12   .38860952  -.00362734    .0112125  -.02560344   .01834876
r13   .40469874  -.00271822   .01089546  -.02407293    .0186365
r14   .42078797  -.00180532   .01059099  -.02256328   .01895263
r15   .43687719   -.0008885   .01030015  -.02107642   .01929943
r16   .45296641   .00003241   .01002409  -.01961445   .01967928
r17   .46905564   .00095757   .00976405  -.01817961   .02009475
r18   .48514486   .00188714    .0095213  -.01677427   .02054855
r19   .50123408   .00282127    .0092972  -.01540091   .02104346
r20    .5173233   .00376013   .00909314  -.01406209   .02158236
r21   .53341253   .00470389   .00891049  -.01276035   .02216813
r22   .54950175   .00565269   .00875063  -.01149824   .02280362
r23   .56559097    .0066067   .00861487  -.01027814   .02349154
r24    .5816802   .00756608   .00850441  -.00910225   .02423441
r25   .59776942   .00853099    .0084203   -.0079725   .02503447
r26   .61385864   .00950158   .00836341  -.00689041   .02589358
r27   .62994787   .01047802   .00833439  -.00585709   .02681313
r28   .64603709   .01146046   .00833362  -.00487314   .02779405
r29   .66212631   .01244905   .00836121  -.00393862   .02883671
r30   .67821554   .01344394   .00841699  -.00305306   .02994094
r31   .69430476   .01444529   .00850054  -.00221546   .03110604
r32   .71039398   .01545324   .00861117  -.00142434   .03233082
r33   .72648321   .01646794     .008748  -.00067782    .0336137
r34   .74257243   .01748953   .00890995   .00002635   .03495272
r35   .75866165   .01851816   .00909585   .00069063   .03634569
r36   .77475088   .01955396    .0093044   .00131767   .03779024
r37    .7908401   .02059706   .00953428   .00191023    .0392839
r38   .80692932   .02164761   .00978414   .00247105   .04082417
r39   .82301855   .02270572   .01005266   .00300287   .04240857
r40   .83910777   .02377152   .01033855   .00350834    .0440347
r41   .85519699   .02484513   .01064057   .00398999   .04570027
r42   .87128621   .02592668   .01095757   .00445024   .04740312
r43   .88737544   .02701626   .01128844   .00489133    .0491412
r44   .90346466     .028114   .01163217   .00531535   .05091264
r45   .91955388   .02921998   .01198783   .00572427    .0527157
r46   .93564311   .03033432   .01235455   .00611985   .05454879
r47   .95173233   .03145711   .01273154   .00650375   .05641047
r48   .96782155   .03258842   .01311809   .00687745    .0582994
r49   .98391078   .03372836   .01351353   .00724233    .0602144
r50           1     .034877   .01391728   .00759963   .06215437

. 
. erase h1.gph

. erase h2.gph

. erase .pdf

. erase temp.dta

. erase pers-temp.dta

. 
.         ***************************** THE END **************************
.         
.         log close
      name:  <unnamed>
       log:  C:\Users\jgw12\Dropbox\Research\PersPartyBook\Data\FKTW-reproduction\C
> h6-Violence.log
  log type:  text
 closed on:  26 Jul 2023, 20:00:37
-----------------------------------------------------------------------------------
